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"shortDescription" : "", + "shape" : "[1]", + "name" : "position5", + "type" : "MultiArray" + } + ], + "generatedClassName" : "cond_step", + "method" : "predict" + } +] \ No newline at end of file diff --git a/v2/english/cond_step.mlmodelc/model.mil b/v2/english/cond_step.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..34d40910177bcb37a247d4cf26128a2d27c2d530 --- /dev/null +++ b/v2/english/cond_step.mlmodelc/model.mil @@ -0,0 +1,1201 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor cache0, tensor cache1, tensor cache2, tensor cache3, tensor cache4, tensor cache5, tensor conditioning, tensor position0, tensor position1, tensor position2, tensor position3, tensor position4, tensor position5) { + tensor norm0_1_bias = const()[name = tensor("norm0_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor norm0_1_weight = const()[name = tensor("norm0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4224)))]; + tensor attn0_in_proj_weight = const()[name = tensor("attn0_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8384)))]; + tensor attn0_out_proj_weight = const()[name = tensor("attn0_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12591360)))]; + tensor norm0_2_bias = const()[name = tensor("norm0_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16785728)))]; + tensor norm0_2_weight = const()[name = tensor("norm0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16789888)))]; + tensor linear0_1_weight = const()[name = tensor("linear0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16794048)))]; + tensor linear0_2_weight = const()[name = tensor("linear0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33571328)))]; + tensor norm1_1_bias = const()[name = tensor("norm1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50348608)))]; + tensor norm1_1_weight = const()[name = tensor("norm1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50352768)))]; + tensor attn1_in_proj_weight = const()[name = tensor("attn1_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50356928)))]; + tensor attn1_out_proj_weight = const()[name = tensor("attn1_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62939904)))]; + tensor norm1_2_bias = const()[name = tensor("norm1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67134272)))]; + tensor norm1_2_weight = const()[name = tensor("norm1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67138432)))]; + tensor linear1_1_weight = const()[name = tensor("linear1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67142592)))]; + tensor linear1_2_weight = const()[name = tensor("linear1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83919872)))]; + tensor norm2_1_bias = const()[name = tensor("norm2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100697152)))]; + tensor norm2_1_weight = const()[name = tensor("norm2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100701312)))]; + tensor attn2_in_proj_weight = const()[name = tensor("attn2_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100705472)))]; + tensor attn2_out_proj_weight = const()[name = tensor("attn2_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113288448)))]; + tensor norm2_2_bias = const()[name = tensor("norm2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117482816)))]; + tensor norm2_2_weight = const()[name = tensor("norm2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117486976)))]; + tensor linear2_1_weight = const()[name = tensor("linear2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117491136)))]; + tensor linear2_2_weight = const()[name = tensor("linear2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134268416)))]; + tensor norm3_1_bias = const()[name = tensor("norm3_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151045696)))]; + tensor norm3_1_weight = const()[name = tensor("norm3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151049856)))]; + tensor attn3_in_proj_weight = const()[name = tensor("attn3_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151054016)))]; + tensor attn3_out_proj_weight = const()[name = tensor("attn3_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163636992)))]; + tensor norm3_2_bias = const()[name = tensor("norm3_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167831360)))]; + tensor norm3_2_weight = const()[name = tensor("norm3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167835520)))]; + tensor linear3_1_weight = const()[name = tensor("linear3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167839680)))]; + tensor linear3_2_weight = const()[name = tensor("linear3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184616960)))]; + tensor norm4_1_bias = const()[name = tensor("norm4_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201394240)))]; + tensor norm4_1_weight = const()[name = tensor("norm4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201398400)))]; + tensor attn4_in_proj_weight = const()[name = tensor("attn4_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201402560)))]; + tensor attn4_out_proj_weight = const()[name = tensor("attn4_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213985536)))]; + tensor norm4_2_bias = const()[name = tensor("norm4_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218179904)))]; + tensor norm4_2_weight = const()[name = tensor("norm4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218184064)))]; + tensor linear4_1_weight = const()[name = tensor("linear4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218188224)))]; + tensor linear4_2_weight = const()[name = tensor("linear4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234965504)))]; + tensor norm5_1_bias = const()[name = tensor("norm5_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251742784)))]; + tensor norm5_1_weight = const()[name = tensor("norm5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251746944)))]; + tensor attn5_in_proj_weight = const()[name = tensor("attn5_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251751104)))]; + tensor var_47 = const()[name = tensor("op_47"), val = tensor(0x1.4f8b58p-17)]; + tensor x_1_axes_0 = const()[name = tensor("x_1_axes_0"), val = tensor([-1])]; + tensor x_1 = layer_norm(axes = x_1_axes_0, beta = norm0_1_bias, epsilon = var_47, gamma = norm0_1_weight, x = conditioning)[name = tensor("x_1")]; + tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264334080)))]; + tensor var_79 = linear(bias = linear_0_bias_0, weight = attn0_in_proj_weight, x = x_1)[name = tensor("linear_0")]; + tensor var_83 = const()[name = tensor("op_83"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_1 = reshape(shape = var_83, x = var_79)[name = tensor("qkv_1")]; + tensor q_1_begin_0 = const()[name = tensor("q_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_1_end_0 = const()[name = tensor("q_1_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_1_end_mask_0 = const()[name = tensor("q_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_1_squeeze_mask_0 = const()[name = tensor("q_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_1 = slice_by_index(begin = q_1_begin_0, end = q_1_end_0, end_mask = q_1_end_mask_0, squeeze_mask = q_1_squeeze_mask_0, x = qkv_1)[name = tensor("q_1")]; + tensor k_1_begin_0 = const()[name = tensor("k_1_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_1_end_0 = const()[name = tensor("k_1_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_1_end_mask_0 = const()[name = tensor("k_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_1_squeeze_mask_0 = const()[name = tensor("k_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_1 = slice_by_index(begin = k_1_begin_0, end = k_1_end_0, end_mask = k_1_end_mask_0, squeeze_mask = k_1_squeeze_mask_0, x = qkv_1)[name = tensor("k_1")]; + tensor v_1_begin_0 = const()[name = tensor("v_1_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_1_end_0 = const()[name = tensor("v_1_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_1_end_mask_0 = const()[name = tensor("v_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_1_squeeze_mask_0 = const()[name = tensor("v_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_1 = slice_by_index(begin = v_1_begin_0, end = v_1_end_0, end_mask = v_1_end_mask_0, squeeze_mask = v_1_squeeze_mask_0, x = qkv_1)[name = tensor("v_1")]; + tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264346432)))]; + tensor var_187 = const()[name = tensor("op_187"), val = tensor([1, 1, 1, 1])]; + tensor ts_5 = reshape(shape = var_187, x = position0)[name = tensor("ts_5")]; + tensor var_191 = const()[name = tensor("op_191"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_1 = reshape(shape = var_191, x = q_1)[name = tensor("q_complex_1")]; + tensor var_195 = const()[name = tensor("op_195"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_1 = reshape(shape = var_195, x = k_1)[name = tensor("k_complex_1")]; + tensor var_199_begin_0 = const()[name = tensor("op_199_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_199_end_0 = const()[name = tensor("op_199_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_199_end_mask_0 = const()[name = tensor("op_199_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_199_squeeze_mask_0 = const()[name = tensor("op_199_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_199 = slice_by_index(begin = var_199_begin_0, end = var_199_end_0, end_mask = var_199_end_mask_0, squeeze_mask = var_199_squeeze_mask_0, x = q_complex_1)[name = tensor("op_199")]; + tensor var_207_begin_0 = const()[name = tensor("op_207_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_207_end_0 = const()[name = tensor("op_207_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_207_end_mask_0 = const()[name = tensor("op_207_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_207_squeeze_mask_0 = const()[name = tensor("op_207_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_207 = slice_by_index(begin = var_207_begin_0, end = var_207_end_0, end_mask = var_207_end_mask_0, squeeze_mask = var_207_squeeze_mask_0, x = q_complex_1)[name = tensor("op_207")]; + tensor var_215_begin_0 = const()[name = tensor("op_215_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_215_end_0 = const()[name = tensor("op_215_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_215_end_mask_0 = const()[name = tensor("op_215_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_215_squeeze_mask_0 = const()[name = tensor("op_215_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_215 = slice_by_index(begin = var_215_begin_0, end = var_215_end_0, end_mask = var_215_end_mask_0, squeeze_mask = var_215_squeeze_mask_0, x = k_complex_1)[name = tensor("op_215")]; + tensor var_223_begin_0 = const()[name = tensor("op_223_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_223_end_0 = const()[name = tensor("op_223_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_223_end_mask_0 = const()[name = tensor("op_223_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_223_squeeze_mask_0 = const()[name = tensor("op_223_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_223 = slice_by_index(begin = var_223_begin_0, end = var_223_end_0, end_mask = var_223_end_mask_0, squeeze_mask = var_223_squeeze_mask_0, x = k_complex_1)[name = tensor("op_223")]; + tensor var_229 = mul(x = freqs_1, y = ts_5)[name = tensor("op_229")]; + tensor rotr_1 = cos(x = var_229)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_229)[name = tensor("roti_1")]; + tensor var_233 = mul(x = var_199, y = rotr_1)[name = tensor("op_233")]; + tensor var_234 = mul(x = var_207, y = roti_1)[name = tensor("op_234")]; + tensor qor_1 = sub(x = var_233, y = var_234)[name = tensor("qor_1")]; + tensor var_237 = mul(x = var_199, y = roti_1)[name = tensor("op_237")]; + tensor var_238 = mul(x = var_207, y = rotr_1)[name = tensor("op_238")]; + tensor qoi_1 = add(x = var_237, y = var_238)[name = tensor("qoi_1")]; + tensor var_241 = mul(x = var_215, y = rotr_1)[name = tensor("op_241")]; + tensor var_242 = mul(x = var_223, y = roti_1)[name = tensor("op_242")]; + tensor kor_1 = sub(x = var_241, y = var_242)[name = tensor("kor_1")]; + tensor var_245 = mul(x = var_215, y = roti_1)[name = tensor("op_245")]; + tensor var_246 = mul(x = var_223, y = rotr_1)[name = tensor("op_246")]; + tensor koi_1 = add(x = var_245, y = var_246)[name = tensor("koi_1")]; + tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; + tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; + tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; + tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; + tensor var_275 = const()[name = tensor("op_275"), val = tensor([1, 1, 16, 64])]; + tensor q_3 = reshape(shape = var_275, x = qo_1)[name = tensor("q_3")]; + tensor var_277 = const()[name = tensor("op_277"), val = tensor([1, 1, 16, 64])]; + tensor k_3 = reshape(shape = var_277, x = ko_1)[name = tensor("k_3")]; + tensor _inversed_299_y_0 = const()[name = tensor("_inversed_299_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_299 = mul(x = ts_5, y = _inversed_299_y_0)[name = tensor("_inversed_299")]; + tensor var_300 = floor(x = _inversed_299)[name = tensor("op_300")]; + tensor var_301 = const()[name = tensor("op_301"), val = tensor(0x1p+9)]; + tensor var_302 = mul(x = var_300, y = var_301)[name = tensor("op_302")]; + tensor write_indices_float_3 = sub(x = ts_5, y = var_302)[name = tensor("write_indices_float_3")]; + tensor var_309_dtype_0 = const()[name = tensor("op_309_dtype_0"), val = tensor("int32")]; + tensor write_indices_1_reps_0 = const()[name = tensor("write_indices_1_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_309 = cast(dtype = var_309_dtype_0, x = write_indices_float_3)[name = tensor("cast_104")]; + tensor write_indices_1 = tile(reps = write_indices_1_reps_0, x = var_309)[name = tensor("write_indices_1")]; + tensor var_317_begin_0 = const()[name = tensor("op_317_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_317_end_0 = const()[name = tensor("op_317_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_317_end_mask_0 = const()[name = tensor("op_317_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_317_squeeze_mask_0 = const()[name = tensor("op_317_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_317 = slice_by_index(begin = var_317_begin_0, end = var_317_end_0, end_mask = var_317_end_mask_0, squeeze_mask = var_317_squeeze_mask_0, x = cache0)[name = tensor("op_317")]; + tensor var_319_axis_0 = const()[name = tensor("op_319_axis_0"), val = tensor(1)]; + tensor var_319_mode_0 = const()[name = tensor("op_319_mode_0"), val = tensor("update")]; + tensor var_319_validate_indices_0 = const()[name = tensor("op_319_validate_indices_0"), val = tensor(false)]; + tensor var_319 = scatter_along_axis(axis = var_319_axis_0, data = var_317, indices = write_indices_1, mode = var_319_mode_0, updates = k_3, validate_indices = var_319_validate_indices_0)[name = tensor("op_319")]; + tensor concat_1 = const()[name = tensor("concat_1"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_2 = const()[name = tensor("concat_2"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_10 = const()[name = tensor("shape_10"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_0 = const()[name = tensor("reduce_prod_0"), val = tensor(1048576)]; + tensor range_1d_0_start_0 = const()[name = tensor("range_1d_0_start_0"), val = tensor(0)]; + tensor range_1d_0_step_0 = const()[name = tensor("range_1d_0_step_0"), val = tensor(1)]; + tensor range_1d_0 = range_1d(end = reduce_prod_0, start = range_1d_0_start_0, step = range_1d_0_step_0)[name = tensor("range_1d_0")]; + tensor reshape_0 = reshape(shape = shape_10, x = range_1d_0)[name = tensor("reshape_0")]; + tensor slice_by_index_0 = slice_by_index(begin = concat_1, begin_mask = new_cache_1_internal_tensor_assign_1_begin_mask_0, end = concat_2, end_mask = new_cache_1_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_1_stride_0, x = reshape_0)[name = tensor("slice_by_index_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([-1])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = slice_by_index_0)[name = tensor("reshape_1")]; + tensor reshape_2_shape_0 = const()[name = tensor("reshape_2_shape_0"), val = tensor([-1])]; + tensor reshape_2 = reshape(shape = reshape_2_shape_0, x = var_319)[name = tensor("reshape_2")]; + tensor reshape_3_shape_0 = const()[name = tensor("reshape_3_shape_0"), val = tensor([-1])]; + tensor reshape_3 = reshape(shape = reshape_3_shape_0, x = cache0)[name = tensor("reshape_3")]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor scatter_0_validate_indices_0 = const()[name = tensor("scatter_0_validate_indices_0"), val = tensor(false)]; + tensor scatter_0 = scatter(axis = scatter_0_axis_0, data = reshape_3, indices = reshape_1, mode = scatter_0_mode_0, updates = reshape_2, validate_indices = scatter_0_validate_indices_0)[name = tensor("scatter_0")]; + tensor reshape_4 = reshape(shape = shape_10, x = scatter_0)[name = tensor("reshape_4")]; + tensor var_327_begin_0 = const()[name = tensor("op_327_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_327_end_0 = const()[name = tensor("op_327_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_327_end_mask_0 = const()[name = tensor("op_327_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_327_squeeze_mask_0 = const()[name = tensor("op_327_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_327 = slice_by_index(begin = var_327_begin_0, end = var_327_end_0, end_mask = var_327_end_mask_0, squeeze_mask = var_327_squeeze_mask_0, x = reshape_4)[name = tensor("op_327")]; + tensor var_329_axis_0 = const()[name = tensor("op_329_axis_0"), val = tensor(1)]; + tensor var_329_mode_0 = const()[name = tensor("op_329_mode_0"), val = tensor("update")]; + tensor var_329_validate_indices_0 = const()[name = tensor("op_329_validate_indices_0"), val = tensor(false)]; + tensor var_329 = scatter_along_axis(axis = var_329_axis_0, data = var_327, indices = write_indices_1, mode = var_329_mode_0, updates = v_1, validate_indices = var_329_validate_indices_0)[name = tensor("op_329")]; + tensor concat_3 = const()[name = tensor("concat_3"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_11 = const()[name = tensor("shape_11"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_1 = const()[name = tensor("reduce_prod_1"), val = tensor(1048576)]; + tensor range_1d_1_start_0 = const()[name = tensor("range_1d_1_start_0"), val = tensor(0)]; + tensor range_1d_1_step_0 = const()[name = tensor("range_1d_1_step_0"), val = tensor(1)]; + tensor range_1d_1 = range_1d(end = reduce_prod_1, start = range_1d_1_start_0, step = range_1d_1_step_0)[name = tensor("range_1d_1")]; + tensor reshape_5 = reshape(shape = shape_11, x = range_1d_1)[name = tensor("reshape_5")]; + tensor slice_by_index_1 = slice_by_index(begin = concat_3, begin_mask = new_cache_1_internal_tensor_assign_2_begin_mask_0, end = concat_4, end_mask = new_cache_1_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_2_stride_0, x = reshape_5)[name = tensor("slice_by_index_1")]; + tensor reshape_6_shape_0 = const()[name = tensor("reshape_6_shape_0"), val = tensor([-1])]; + tensor reshape_6 = reshape(shape = reshape_6_shape_0, x = slice_by_index_1)[name = tensor("reshape_6")]; + tensor reshape_7_shape_0 = const()[name = tensor("reshape_7_shape_0"), val = tensor([-1])]; + tensor reshape_7 = reshape(shape = reshape_7_shape_0, x = var_329)[name = tensor("reshape_7")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([-1])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = reshape_4)[name = tensor("reshape_8")]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor scatter_1_validate_indices_0 = const()[name = tensor("scatter_1_validate_indices_0"), val = tensor(false)]; + tensor scatter_1 = scatter(axis = scatter_1_axis_0, data = reshape_8, indices = reshape_6, mode = scatter_1_mode_0, updates = reshape_7, validate_indices = scatter_1_validate_indices_0)[name = tensor("scatter_1")]; + tensor new_cache_1_internal_tensor_assign_2 = reshape(shape = shape_11, x = scatter_1)[name = tensor("reshape_9")]; + tensor keys_1_begin_0 = const()[name = tensor("keys_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_1_end_0 = const()[name = tensor("keys_1_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_1_end_mask_0 = const()[name = tensor("keys_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_1_squeeze_mask_0 = const()[name = tensor("keys_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_1 = slice_by_index(begin = keys_1_begin_0, end = keys_1_end_0, end_mask = keys_1_end_mask_0, squeeze_mask = keys_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("keys_1")]; + tensor values_1_begin_0 = const()[name = tensor("values_1_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_1_end_0 = const()[name = tensor("values_1_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_1_end_mask_0 = const()[name = tensor("values_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_1_squeeze_mask_0 = const()[name = tensor("values_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_1 = slice_by_index(begin = values_1_begin_0, end = values_1_end_0, end_mask = values_1_end_mask_0, squeeze_mask = values_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("values_1")]; + tensor var_341 = not_equal(x = keys_1, y = keys_1)[name = tensor("op_341")]; + tensor var_347 = const()[name = tensor("op_347"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264346624)))]; + tensor keys_3 = select(a = var_347, b = keys_1, cond = var_341)[name = tensor("keys_3")]; + tensor var_349 = not_equal(x = values_1, y = values_1)[name = tensor("op_349")]; + tensor values_3 = select(a = var_347, b = values_1, cond = var_349)[name = tensor("values_3")]; + tensor var_373 = const()[name = tensor("op_373"), val = tensor([0, 2, 1, 3])]; + tensor var_386 = const()[name = tensor("op_386"), val = tensor([1, 1, 1])]; + tensor var_387 = reshape(shape = var_386, x = position0)[name = tensor("op_387")]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor(0x1p+0)]; + tensor valid_len_1 = add(x = var_387, y = var_404)[name = tensor("valid_len_1")]; + tensor k_positions_1_promoted = const()[name = tensor("k_positions_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266443840)))]; + tensor valid_mask_1 = less(x = k_positions_1_promoted, y = valid_len_1)[name = tensor("valid_mask_1")]; + tensor causal_mask_1 = less_equal(x = k_positions_1_promoted, y = var_387)[name = tensor("causal_mask_1")]; + tensor attn_mask_1 = logical_and(x = valid_mask_1, y = causal_mask_1)[name = tensor("attn_mask_1")]; + tensor attn_mask_3_axes_0 = const()[name = tensor("attn_mask_3_axes_0"), val = tensor([1])]; + tensor attn_mask_3 = expand_dims(axes = attn_mask_3_axes_0, x = attn_mask_1)[name = tensor("attn_mask_3")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([0x1.fffe5cp-4])]; + tensor var_422_transpose_x_0 = const()[name = tensor("op_422_transpose_x_0"), val = tensor(false)]; + tensor var_422_transpose_y_0 = const()[name = tensor("op_422_transpose_y_0"), val = tensor(false)]; + tensor transpose_15_perm_0 = const()[name = tensor("transpose_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_16_perm_0 = const()[name = tensor("transpose_16_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_16 = transpose(perm = transpose_16_perm_0, x = keys_3)[name = tensor("transpose_42")]; + tensor transpose_15 = transpose(perm = transpose_15_perm_0, x = q_3)[name = tensor("transpose_43")]; + tensor var_422 = matmul(transpose_x = var_422_transpose_x_0, transpose_y = var_422_transpose_y_0, x = transpose_15, y = transpose_16)[name = tensor("op_422")]; + tensor attn_weights_1 = mul(x = var_422, y = var_416)[name = tensor("attn_weights_1")]; + tensor var_424 = logical_not(x = attn_mask_3)[name = tensor("op_424")]; + tensor var_425 = const()[name = tensor("op_425"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_3 = select(a = var_425, b = attn_weights_1, cond = var_424)[name = tensor("attn_weights_3")]; + tensor var_427 = const()[name = tensor("op_427"), val = tensor(-1)]; + tensor attn_weights_5 = softmax(axis = var_427, x = attn_weights_3)[name = tensor("attn_weights_5")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor values_5 = transpose(perm = var_373, x = values_3)[name = tensor("transpose_44")]; + tensor attn_output_1 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = attn_weights_5, y = values_5)[name = tensor("attn_output_1")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([0, 2, 1, 3])]; + tensor var_438 = const()[name = tensor("op_438"), val = tensor([1, 1, 1024])]; + tensor var_436 = transpose(perm = var_435, x = attn_output_1)[name = tensor("transpose_41")]; + tensor input_3 = reshape(shape = var_438, x = var_436)[name = tensor("input_3")]; + tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266445952)))]; + tensor attn_out_1 = linear(bias = linear_1_bias_0, weight = attn0_out_proj_weight, x = input_3)[name = tensor("linear_1")]; + tensor var_444 = const()[name = tensor("op_444"), val = tensor(0x1p+0)]; + tensor var_445 = add(x = position0, y = var_444)[name = tensor("op_445")]; + tensor input_5 = add(x = conditioning, y = attn_out_1)[name = tensor("input_5")]; + tensor var_449 = const()[name = tensor("op_449"), val = tensor(0x1.4f8b58p-17)]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7 = layer_norm(axes = input_7_axes_0, beta = norm0_2_bias, epsilon = var_449, gamma = norm0_2_weight, x = input_5)[name = tensor("input_7")]; + tensor linear_2_bias_0 = const()[name = tensor("linear_2_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266450112)))]; + tensor var_457 = linear(bias = linear_2_bias_0, weight = linear0_1_weight, x = input_7)[name = tensor("linear_2")]; + tensor input_9_mode_0 = const()[name = tensor("input_9_mode_0"), val = tensor("EXACT")]; + tensor input_9 = gelu(mode = input_9_mode_0, x = var_457)[name = tensor("input_9")]; + tensor ffn_out_1 = linear(bias = linear_1_bias_0, weight = linear0_2_weight, x = input_9)[name = tensor("linear_3")]; + tensor input_11 = add(x = input_5, y = ffn_out_1)[name = tensor("input_11")]; + tensor var_466 = const()[name = tensor("op_466"), val = tensor(0x1.4f8b58p-17)]; + tensor x_3_axes_0 = const()[name = tensor("x_3_axes_0"), val = tensor([-1])]; + tensor x_3 = layer_norm(axes = x_3_axes_0, beta = norm1_1_bias, epsilon = var_466, gamma = norm1_1_weight, x = input_11)[name = tensor("x_3")]; + tensor var_498 = linear(bias = linear_0_bias_0, weight = attn1_in_proj_weight, x = x_3)[name = tensor("linear_4")]; + tensor var_502 = const()[name = tensor("op_502"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_3 = reshape(shape = var_502, x = var_498)[name = tensor("qkv_3")]; + tensor q_7_begin_0 = const()[name = tensor("q_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_7_end_0 = const()[name = tensor("q_7_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_7_end_mask_0 = const()[name = tensor("q_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_7_squeeze_mask_0 = const()[name = tensor("q_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_7 = slice_by_index(begin = q_7_begin_0, end = q_7_end_0, end_mask = q_7_end_mask_0, squeeze_mask = q_7_squeeze_mask_0, x = qkv_3)[name = tensor("q_7")]; + tensor k_5_begin_0 = const()[name = tensor("k_5_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_5_end_0 = const()[name = tensor("k_5_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_5_end_mask_0 = const()[name = tensor("k_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_5_squeeze_mask_0 = const()[name = tensor("k_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_5 = slice_by_index(begin = k_5_begin_0, end = k_5_end_0, end_mask = k_5_end_mask_0, squeeze_mask = k_5_squeeze_mask_0, x = qkv_3)[name = tensor("k_5")]; + tensor v_3_begin_0 = const()[name = tensor("v_3_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_3_end_0 = const()[name = tensor("v_3_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_3_end_mask_0 = const()[name = tensor("v_3_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_3_squeeze_mask_0 = const()[name = tensor("v_3_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_3 = slice_by_index(begin = v_3_begin_0, end = v_3_end_0, end_mask = v_3_end_mask_0, squeeze_mask = v_3_squeeze_mask_0, x = qkv_3)[name = tensor("v_3")]; + tensor freqs_3 = const()[name = tensor("freqs_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266466560)))]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1, 1, 1])]; + tensor ts_11 = reshape(shape = var_606, x = position1)[name = tensor("ts_11")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_3 = reshape(shape = var_610, x = q_7)[name = tensor("q_complex_3")]; + tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_3 = reshape(shape = var_614, x = k_5)[name = tensor("k_complex_3")]; + tensor var_618_begin_0 = const()[name = tensor("op_618_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_618_end_0 = const()[name = tensor("op_618_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_618_end_mask_0 = const()[name = tensor("op_618_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_618_squeeze_mask_0 = const()[name = tensor("op_618_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_618 = slice_by_index(begin = var_618_begin_0, end = var_618_end_0, end_mask = var_618_end_mask_0, squeeze_mask = var_618_squeeze_mask_0, x = q_complex_3)[name = tensor("op_618")]; + tensor var_626_begin_0 = const()[name = tensor("op_626_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_626_end_0 = const()[name = tensor("op_626_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_626_end_mask_0 = const()[name = tensor("op_626_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_626_squeeze_mask_0 = const()[name = tensor("op_626_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_626 = slice_by_index(begin = var_626_begin_0, end = var_626_end_0, end_mask = var_626_end_mask_0, squeeze_mask = var_626_squeeze_mask_0, x = q_complex_3)[name = tensor("op_626")]; + tensor var_634_begin_0 = const()[name = tensor("op_634_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_634_end_0 = const()[name = tensor("op_634_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_634_end_mask_0 = const()[name = tensor("op_634_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_634_squeeze_mask_0 = const()[name = tensor("op_634_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_634 = slice_by_index(begin = var_634_begin_0, end = var_634_end_0, end_mask = var_634_end_mask_0, squeeze_mask = var_634_squeeze_mask_0, x = k_complex_3)[name = tensor("op_634")]; + tensor var_642_begin_0 = const()[name = tensor("op_642_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_642_end_0 = const()[name = tensor("op_642_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_642_end_mask_0 = const()[name = tensor("op_642_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_642_squeeze_mask_0 = const()[name = tensor("op_642_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_642 = slice_by_index(begin = var_642_begin_0, end = var_642_end_0, end_mask = var_642_end_mask_0, squeeze_mask = var_642_squeeze_mask_0, x = k_complex_3)[name = tensor("op_642")]; + tensor var_648 = mul(x = freqs_3, y = ts_11)[name = tensor("op_648")]; + tensor rotr_3 = cos(x = var_648)[name = tensor("rotr_3")]; + tensor roti_3 = sin(x = var_648)[name = tensor("roti_3")]; + tensor var_652 = mul(x = var_618, y = rotr_3)[name = tensor("op_652")]; + tensor var_653 = mul(x = var_626, y = roti_3)[name = tensor("op_653")]; + tensor qor_5 = sub(x = var_652, y = var_653)[name = tensor("qor_5")]; + tensor var_656 = mul(x = var_618, y = roti_3)[name = tensor("op_656")]; + tensor var_657 = mul(x = var_626, y = rotr_3)[name = tensor("op_657")]; + tensor qoi_5 = add(x = var_656, y = var_657)[name = tensor("qoi_5")]; + tensor var_660 = mul(x = var_634, y = rotr_3)[name = tensor("op_660")]; + tensor var_661 = mul(x = var_642, y = roti_3)[name = tensor("op_661")]; + tensor kor_5 = sub(x = var_660, y = var_661)[name = tensor("kor_5")]; + tensor var_664 = mul(x = var_634, y = roti_3)[name = tensor("op_664")]; + tensor var_665 = mul(x = var_642, y = rotr_3)[name = tensor("op_665")]; + tensor koi_5 = add(x = var_664, y = var_665)[name = tensor("koi_5")]; + tensor qo_3_axis_0 = const()[name = tensor("qo_3_axis_0"), val = tensor(-1)]; + tensor qo_3 = stack(axis = qo_3_axis_0, values = (qor_5, qoi_5))[name = tensor("qo_3")]; + tensor ko_3_axis_0 = const()[name = tensor("ko_3_axis_0"), val = tensor(-1)]; + tensor ko_3 = stack(axis = ko_3_axis_0, values = (kor_5, koi_5))[name = tensor("ko_3")]; + tensor var_694 = const()[name = tensor("op_694"), val = tensor([1, 1, 16, 64])]; + tensor q_9 = reshape(shape = var_694, x = qo_3)[name = tensor("q_9")]; + tensor var_696 = const()[name = tensor("op_696"), val = tensor([1, 1, 16, 64])]; + tensor k_7 = reshape(shape = var_696, x = ko_3)[name = tensor("k_7")]; + tensor _inversed_718_y_0 = const()[name = tensor("_inversed_718_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_718 = mul(x = ts_11, y = _inversed_718_y_0)[name = tensor("_inversed_718")]; + tensor var_719 = floor(x = _inversed_718)[name = tensor("op_719")]; + tensor var_720 = const()[name = tensor("op_720"), val = tensor(0x1p+9)]; + tensor var_721 = mul(x = var_719, y = var_720)[name = tensor("op_721")]; + tensor write_indices_float_7 = sub(x = ts_11, y = var_721)[name = tensor("write_indices_float_7")]; + tensor var_728_dtype_0 = const()[name = tensor("op_728_dtype_0"), val = tensor("int32")]; + tensor write_indices_3_reps_0 = const()[name = tensor("write_indices_3_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_728 = cast(dtype = var_728_dtype_0, x = write_indices_float_7)[name = tensor("cast_103")]; + tensor write_indices_3 = tile(reps = write_indices_3_reps_0, x = var_728)[name = tensor("write_indices_3")]; + tensor var_736_begin_0 = const()[name = tensor("op_736_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_736_end_0 = const()[name = tensor("op_736_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_736_end_mask_0 = const()[name = tensor("op_736_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_736_squeeze_mask_0 = const()[name = tensor("op_736_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_736 = slice_by_index(begin = var_736_begin_0, end = var_736_end_0, end_mask = var_736_end_mask_0, squeeze_mask = var_736_squeeze_mask_0, x = cache1)[name = tensor("op_736")]; + tensor var_738_axis_0 = const()[name = tensor("op_738_axis_0"), val = tensor(1)]; + tensor var_738_mode_0 = const()[name = tensor("op_738_mode_0"), val = tensor("update")]; + tensor var_738_validate_indices_0 = const()[name = tensor("op_738_validate_indices_0"), val = tensor(false)]; + tensor var_738 = scatter_along_axis(axis = var_738_axis_0, data = var_736, indices = write_indices_3, mode = var_738_mode_0, updates = k_7, validate_indices = var_738_validate_indices_0)[name = tensor("op_738")]; + tensor concat_8 = const()[name = tensor("concat_8"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_9 = const()[name = tensor("concat_9"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_12 = const()[name = tensor("shape_12"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_2 = const()[name = tensor("reduce_prod_2"), val = tensor(1048576)]; + tensor range_1d_2_start_0 = const()[name = tensor("range_1d_2_start_0"), val = tensor(0)]; + tensor range_1d_2_step_0 = const()[name = tensor("range_1d_2_step_0"), val = tensor(1)]; + tensor range_1d_2 = range_1d(end = reduce_prod_2, start = range_1d_2_start_0, step = range_1d_2_step_0)[name = tensor("range_1d_2")]; + tensor reshape_10 = reshape(shape = shape_12, x = range_1d_2)[name = tensor("reshape_10")]; + tensor slice_by_index_2 = slice_by_index(begin = concat_8, begin_mask = new_cache_3_internal_tensor_assign_1_begin_mask_0, end = concat_9, end_mask = new_cache_3_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_1_stride_0, x = reshape_10)[name = tensor("slice_by_index_2")]; + tensor reshape_11_shape_0 = const()[name = tensor("reshape_11_shape_0"), val = tensor([-1])]; + tensor reshape_11 = reshape(shape = reshape_11_shape_0, x = slice_by_index_2)[name = tensor("reshape_11")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([-1])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = var_738)[name = tensor("reshape_12")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([-1])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = cache1)[name = tensor("reshape_13")]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2_validate_indices_0 = const()[name = tensor("scatter_2_validate_indices_0"), val = tensor(false)]; + tensor scatter_2 = scatter(axis = scatter_2_axis_0, data = reshape_13, indices = reshape_11, mode = scatter_2_mode_0, updates = reshape_12, validate_indices = scatter_2_validate_indices_0)[name = tensor("scatter_2")]; + tensor reshape_14 = reshape(shape = shape_12, x = scatter_2)[name = tensor("reshape_14")]; + tensor var_746_begin_0 = const()[name = tensor("op_746_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_746_end_0 = const()[name = tensor("op_746_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_746_end_mask_0 = const()[name = tensor("op_746_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_746_squeeze_mask_0 = const()[name = tensor("op_746_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_746 = slice_by_index(begin = var_746_begin_0, end = var_746_end_0, end_mask = var_746_end_mask_0, squeeze_mask = var_746_squeeze_mask_0, x = reshape_14)[name = tensor("op_746")]; + tensor var_748_axis_0 = const()[name = tensor("op_748_axis_0"), val = tensor(1)]; + tensor var_748_mode_0 = const()[name = tensor("op_748_mode_0"), val = tensor("update")]; + tensor var_748_validate_indices_0 = const()[name = tensor("op_748_validate_indices_0"), val = tensor(false)]; + tensor var_748 = scatter_along_axis(axis = var_748_axis_0, data = var_746, indices = write_indices_3, mode = var_748_mode_0, updates = v_3, validate_indices = var_748_validate_indices_0)[name = tensor("op_748")]; + tensor concat_10 = const()[name = tensor("concat_10"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_11 = const()[name = tensor("concat_11"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_13 = const()[name = tensor("shape_13"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_3 = const()[name = tensor("reduce_prod_3"), val = tensor(1048576)]; + tensor range_1d_3_start_0 = const()[name = tensor("range_1d_3_start_0"), val = tensor(0)]; + tensor range_1d_3_step_0 = const()[name = tensor("range_1d_3_step_0"), val = tensor(1)]; + tensor range_1d_3 = range_1d(end = reduce_prod_3, start = range_1d_3_start_0, step = range_1d_3_step_0)[name = tensor("range_1d_3")]; + tensor reshape_15 = reshape(shape = shape_13, x = range_1d_3)[name = tensor("reshape_15")]; + tensor slice_by_index_3 = slice_by_index(begin = concat_10, begin_mask = new_cache_3_internal_tensor_assign_2_begin_mask_0, end = concat_11, end_mask = new_cache_3_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_2_stride_0, x = reshape_15)[name = tensor("slice_by_index_3")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([-1])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = slice_by_index_3)[name = tensor("reshape_16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([-1])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = var_748)[name = tensor("reshape_17")]; + tensor reshape_18_shape_0 = const()[name = tensor("reshape_18_shape_0"), val = tensor([-1])]; + tensor reshape_18 = reshape(shape = reshape_18_shape_0, x = reshape_14)[name = tensor("reshape_18")]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3_validate_indices_0 = const()[name = tensor("scatter_3_validate_indices_0"), val = tensor(false)]; + tensor scatter_3 = scatter(axis = scatter_3_axis_0, data = reshape_18, indices = reshape_16, mode = scatter_3_mode_0, updates = reshape_17, validate_indices = scatter_3_validate_indices_0)[name = tensor("scatter_3")]; + tensor new_cache_3_internal_tensor_assign_2 = reshape(shape = shape_13, x = scatter_3)[name = tensor("reshape_19")]; + tensor keys_7_begin_0 = const()[name = tensor("keys_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_7_end_0 = const()[name = tensor("keys_7_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_7_end_mask_0 = const()[name = tensor("keys_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_7_squeeze_mask_0 = const()[name = tensor("keys_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_7 = slice_by_index(begin = keys_7_begin_0, end = keys_7_end_0, end_mask = keys_7_end_mask_0, squeeze_mask = keys_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("keys_7")]; + tensor values_7_begin_0 = const()[name = tensor("values_7_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_7_end_0 = const()[name = tensor("values_7_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_7_end_mask_0 = const()[name = tensor("values_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_7_squeeze_mask_0 = const()[name = tensor("values_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_7 = slice_by_index(begin = values_7_begin_0, end = values_7_end_0, end_mask = values_7_end_mask_0, squeeze_mask = values_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("values_7")]; + tensor var_760 = not_equal(x = keys_7, y = keys_7)[name = tensor("op_760")]; + tensor keys_9 = select(a = var_347, b = keys_7, cond = var_760)[name = tensor("keys_9")]; + tensor var_768 = not_equal(x = values_7, y = values_7)[name = tensor("op_768")]; + tensor values_9 = select(a = var_347, b = values_7, cond = var_768)[name = tensor("values_9")]; + tensor var_792 = const()[name = tensor("op_792"), val = tensor([0, 2, 1, 3])]; + tensor var_805 = const()[name = tensor("op_805"), val = tensor([1, 1, 1])]; + tensor var_806 = reshape(shape = var_805, x = position1)[name = tensor("op_806")]; + tensor var_823 = const()[name = tensor("op_823"), val = tensor(0x1p+0)]; + tensor valid_len_3 = add(x = var_806, y = var_823)[name = tensor("valid_len_3")]; + tensor valid_mask_3 = less(x = k_positions_1_promoted, y = valid_len_3)[name = tensor("valid_mask_3")]; + tensor causal_mask_3 = less_equal(x = k_positions_1_promoted, y = var_806)[name = tensor("causal_mask_3")]; + tensor attn_mask_5 = logical_and(x = valid_mask_3, y = causal_mask_3)[name = tensor("attn_mask_5")]; + tensor attn_mask_7_axes_0 = const()[name = tensor("attn_mask_7_axes_0"), val = tensor([1])]; + tensor attn_mask_7 = expand_dims(axes = attn_mask_7_axes_0, x = attn_mask_5)[name = tensor("attn_mask_7")]; + tensor var_835 = const()[name = tensor("op_835"), val = tensor([0x1.fffe5cp-4])]; + tensor var_841_transpose_x_0 = const()[name = tensor("op_841_transpose_x_0"), val = tensor(false)]; + tensor var_841_transpose_y_0 = const()[name = tensor("op_841_transpose_y_0"), val = tensor(false)]; + tensor transpose_17_perm_0 = const()[name = tensor("transpose_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_18_perm_0 = const()[name = tensor("transpose_18_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_18 = transpose(perm = transpose_18_perm_0, x = keys_9)[name = tensor("transpose_38")]; + tensor transpose_17 = transpose(perm = transpose_17_perm_0, x = q_9)[name = tensor("transpose_39")]; + tensor var_841 = matmul(transpose_x = var_841_transpose_x_0, transpose_y = var_841_transpose_y_0, x = transpose_17, y = transpose_18)[name = tensor("op_841")]; + tensor attn_weights_7 = mul(x = var_841, y = var_835)[name = tensor("attn_weights_7")]; + tensor var_843 = logical_not(x = attn_mask_7)[name = tensor("op_843")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_9 = select(a = var_844, b = attn_weights_7, cond = var_843)[name = tensor("attn_weights_9")]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor(-1)]; + tensor attn_weights_11 = softmax(axis = var_846, x = attn_weights_9)[name = tensor("attn_weights_11")]; + tensor attn_output_3_transpose_x_0 = const()[name = tensor("attn_output_3_transpose_x_0"), val = tensor(false)]; + tensor attn_output_3_transpose_y_0 = const()[name = tensor("attn_output_3_transpose_y_0"), val = tensor(false)]; + tensor values_11 = transpose(perm = var_792, x = values_9)[name = tensor("transpose_40")]; + tensor attn_output_3 = matmul(transpose_x = attn_output_3_transpose_x_0, transpose_y = attn_output_3_transpose_y_0, x = attn_weights_11, y = values_11)[name = tensor("attn_output_3")]; + tensor var_854 = const()[name = tensor("op_854"), val = tensor([0, 2, 1, 3])]; + tensor var_857 = const()[name = tensor("op_857"), val = tensor([1, 1, 1024])]; + tensor var_855 = transpose(perm = var_854, x = attn_output_3)[name = tensor("transpose_37")]; + tensor input_13 = reshape(shape = var_857, x = var_855)[name = tensor("input_13")]; + tensor attn_out_3 = linear(bias = linear_1_bias_0, weight = attn1_out_proj_weight, x = input_13)[name = tensor("linear_5")]; + tensor var_863 = const()[name = tensor("op_863"), val = tensor(0x1p+0)]; + tensor var_864 = add(x = position1, y = var_863)[name = tensor("op_864")]; + tensor input_15 = add(x = input_11, y = attn_out_3)[name = tensor("input_15")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor(0x1.4f8b58p-17)]; + tensor input_17_axes_0 = const()[name = tensor("input_17_axes_0"), val = tensor([-1])]; + tensor input_17 = layer_norm(axes = input_17_axes_0, beta = norm1_2_bias, epsilon = var_868, gamma = norm1_2_weight, x = input_15)[name = tensor("input_17")]; + tensor var_876 = linear(bias = linear_2_bias_0, weight = linear1_1_weight, x = input_17)[name = tensor("linear_6")]; + tensor input_19_mode_0 = const()[name = tensor("input_19_mode_0"), val = tensor("EXACT")]; + tensor input_19 = gelu(mode = input_19_mode_0, x = var_876)[name = tensor("input_19")]; + tensor ffn_out_3 = linear(bias = linear_1_bias_0, weight = linear1_2_weight, x = input_19)[name = tensor("linear_7")]; + tensor input_21 = add(x = input_15, y = ffn_out_3)[name = tensor("input_21")]; + tensor var_885 = const()[name = tensor("op_885"), val = tensor(0x1.4f8b58p-17)]; + tensor x_5_axes_0 = const()[name = tensor("x_5_axes_0"), val = tensor([-1])]; + tensor x_5 = layer_norm(axes = x_5_axes_0, beta = norm2_1_bias, epsilon = var_885, gamma = norm2_1_weight, x = input_21)[name = tensor("x_5")]; + tensor var_917 = linear(bias = linear_0_bias_0, weight = attn2_in_proj_weight, x = x_5)[name = tensor("linear_8")]; + tensor var_921 = const()[name = tensor("op_921"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_5 = reshape(shape = var_921, x = var_917)[name = tensor("qkv_5")]; + tensor q_13_begin_0 = const()[name = tensor("q_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_13_end_0 = const()[name = tensor("q_13_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_13_end_mask_0 = const()[name = tensor("q_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_13_squeeze_mask_0 = const()[name = tensor("q_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_13 = slice_by_index(begin = q_13_begin_0, end = q_13_end_0, end_mask = q_13_end_mask_0, squeeze_mask = q_13_squeeze_mask_0, x = qkv_5)[name = tensor("q_13")]; + tensor k_9_begin_0 = const()[name = tensor("k_9_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_9_end_0 = const()[name = tensor("k_9_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_9_end_mask_0 = const()[name = tensor("k_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_9_squeeze_mask_0 = const()[name = tensor("k_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_9 = slice_by_index(begin = k_9_begin_0, end = k_9_end_0, end_mask = k_9_end_mask_0, squeeze_mask = k_9_squeeze_mask_0, x = qkv_5)[name = tensor("k_9")]; + tensor v_5_begin_0 = const()[name = tensor("v_5_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_5_end_0 = const()[name = tensor("v_5_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_5_end_mask_0 = const()[name = tensor("v_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_5_squeeze_mask_0 = const()[name = tensor("v_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_5 = slice_by_index(begin = v_5_begin_0, end = v_5_end_0, end_mask = v_5_end_mask_0, squeeze_mask = v_5_squeeze_mask_0, x = qkv_5)[name = tensor("v_5")]; + tensor freqs_5 = const()[name = tensor("freqs_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266466752)))]; + tensor var_1025 = const()[name = tensor("op_1025"), val = tensor([1, 1, 1, 1])]; + tensor ts_17 = reshape(shape = var_1025, x = position2)[name = tensor("ts_17")]; + tensor var_1029 = const()[name = tensor("op_1029"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_5 = reshape(shape = var_1029, x = q_13)[name = tensor("q_complex_5")]; + tensor var_1033 = const()[name = tensor("op_1033"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_5 = reshape(shape = var_1033, x = k_9)[name = tensor("k_complex_5")]; + tensor var_1037_begin_0 = const()[name = tensor("op_1037_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1037_end_0 = const()[name = tensor("op_1037_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1037_end_mask_0 = const()[name = tensor("op_1037_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1037_squeeze_mask_0 = const()[name = tensor("op_1037_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1037 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1037")]; + tensor var_1045_begin_0 = const()[name = tensor("op_1045_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1045_end_0 = const()[name = tensor("op_1045_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1045_end_mask_0 = const()[name = tensor("op_1045_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1045_squeeze_mask_0 = const()[name = tensor("op_1045_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1045 = slice_by_index(begin = var_1045_begin_0, end = var_1045_end_0, end_mask = var_1045_end_mask_0, squeeze_mask = var_1045_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1045")]; + tensor var_1053_begin_0 = const()[name = tensor("op_1053_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1053_end_0 = const()[name = tensor("op_1053_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1053_end_mask_0 = const()[name = tensor("op_1053_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1053_squeeze_mask_0 = const()[name = tensor("op_1053_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1053 = slice_by_index(begin = var_1053_begin_0, end = var_1053_end_0, end_mask = var_1053_end_mask_0, squeeze_mask = var_1053_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1053")]; + tensor var_1061_begin_0 = const()[name = tensor("op_1061_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1061_end_0 = const()[name = tensor("op_1061_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1061_end_mask_0 = const()[name = tensor("op_1061_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1061_squeeze_mask_0 = const()[name = tensor("op_1061_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1061 = slice_by_index(begin = var_1061_begin_0, end = var_1061_end_0, end_mask = var_1061_end_mask_0, squeeze_mask = var_1061_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1061")]; + tensor var_1067 = mul(x = freqs_5, y = ts_17)[name = tensor("op_1067")]; + tensor rotr_5 = cos(x = var_1067)[name = tensor("rotr_5")]; + tensor roti_5 = sin(x = var_1067)[name = tensor("roti_5")]; + tensor var_1071 = mul(x = var_1037, y = rotr_5)[name = tensor("op_1071")]; + tensor var_1072 = mul(x = var_1045, y = roti_5)[name = tensor("op_1072")]; + tensor qor_9 = sub(x = var_1071, y = var_1072)[name = tensor("qor_9")]; + tensor var_1075 = mul(x = var_1037, y = roti_5)[name = tensor("op_1075")]; + tensor var_1076 = mul(x = var_1045, y = rotr_5)[name = tensor("op_1076")]; + tensor qoi_9 = add(x = var_1075, y = var_1076)[name = tensor("qoi_9")]; + tensor var_1079 = mul(x = var_1053, y = rotr_5)[name = tensor("op_1079")]; + tensor var_1080 = mul(x = var_1061, y = roti_5)[name = tensor("op_1080")]; + tensor kor_9 = sub(x = var_1079, y = var_1080)[name = tensor("kor_9")]; + tensor var_1083 = mul(x = var_1053, y = roti_5)[name = tensor("op_1083")]; + tensor var_1084 = mul(x = var_1061, y = rotr_5)[name = tensor("op_1084")]; + tensor koi_9 = add(x = var_1083, y = var_1084)[name = tensor("koi_9")]; + tensor qo_5_axis_0 = const()[name = tensor("qo_5_axis_0"), val = tensor(-1)]; + tensor qo_5 = stack(axis = qo_5_axis_0, values = (qor_9, qoi_9))[name = tensor("qo_5")]; + tensor ko_5_axis_0 = const()[name = tensor("ko_5_axis_0"), val = tensor(-1)]; + tensor ko_5 = stack(axis = ko_5_axis_0, values = (kor_9, koi_9))[name = tensor("ko_5")]; + tensor var_1113 = const()[name = tensor("op_1113"), val = tensor([1, 1, 16, 64])]; + tensor q_15 = reshape(shape = var_1113, x = qo_5)[name = tensor("q_15")]; + tensor var_1115 = const()[name = tensor("op_1115"), val = tensor([1, 1, 16, 64])]; + tensor k_11 = reshape(shape = var_1115, x = ko_5)[name = tensor("k_11")]; + tensor _inversed_1137_y_0 = const()[name = tensor("_inversed_1137_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1137 = mul(x = ts_17, y = _inversed_1137_y_0)[name = tensor("_inversed_1137")]; + tensor var_1138 = floor(x = _inversed_1137)[name = tensor("op_1138")]; + tensor var_1139 = const()[name = tensor("op_1139"), val = tensor(0x1p+9)]; + tensor var_1140 = mul(x = var_1138, y = var_1139)[name = tensor("op_1140")]; + tensor write_indices_float_11 = sub(x = ts_17, y = var_1140)[name = tensor("write_indices_float_11")]; + tensor var_1147_dtype_0 = const()[name = tensor("op_1147_dtype_0"), val = tensor("int32")]; + tensor write_indices_5_reps_0 = const()[name = tensor("write_indices_5_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1147 = cast(dtype = var_1147_dtype_0, x = write_indices_float_11)[name = tensor("cast_102")]; + tensor write_indices_5 = tile(reps = write_indices_5_reps_0, x = var_1147)[name = tensor("write_indices_5")]; + tensor var_1155_begin_0 = const()[name = tensor("op_1155_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1155_end_0 = const()[name = tensor("op_1155_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1155_end_mask_0 = const()[name = tensor("op_1155_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1155_squeeze_mask_0 = const()[name = tensor("op_1155_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1155 = slice_by_index(begin = var_1155_begin_0, end = var_1155_end_0, end_mask = var_1155_end_mask_0, squeeze_mask = var_1155_squeeze_mask_0, x = cache2)[name = tensor("op_1155")]; + tensor var_1157_axis_0 = const()[name = tensor("op_1157_axis_0"), val = tensor(1)]; + tensor var_1157_mode_0 = const()[name = tensor("op_1157_mode_0"), val = tensor("update")]; + tensor var_1157_validate_indices_0 = const()[name = tensor("op_1157_validate_indices_0"), val = tensor(false)]; + tensor var_1157 = scatter_along_axis(axis = var_1157_axis_0, data = var_1155, indices = write_indices_5, mode = var_1157_mode_0, updates = k_11, validate_indices = var_1157_validate_indices_0)[name = tensor("op_1157")]; + tensor concat_15 = const()[name = tensor("concat_15"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_16 = const()[name = tensor("concat_16"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_14 = const()[name = tensor("shape_14"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_4 = const()[name = tensor("reduce_prod_4"), val = tensor(1048576)]; + tensor range_1d_4_start_0 = const()[name = tensor("range_1d_4_start_0"), val = tensor(0)]; + tensor range_1d_4_step_0 = const()[name = tensor("range_1d_4_step_0"), val = tensor(1)]; + tensor range_1d_4 = range_1d(end = reduce_prod_4, start = range_1d_4_start_0, step = range_1d_4_step_0)[name = tensor("range_1d_4")]; + tensor reshape_20 = reshape(shape = shape_14, x = range_1d_4)[name = tensor("reshape_20")]; + tensor slice_by_index_4 = slice_by_index(begin = concat_15, begin_mask = new_cache_5_internal_tensor_assign_1_begin_mask_0, end = concat_16, end_mask = new_cache_5_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_1_stride_0, x = reshape_20)[name = tensor("slice_by_index_4")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([-1])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = slice_by_index_4)[name = tensor("reshape_21")]; + tensor reshape_22_shape_0 = const()[name = tensor("reshape_22_shape_0"), val = tensor([-1])]; + tensor reshape_22 = reshape(shape = reshape_22_shape_0, x = var_1157)[name = tensor("reshape_22")]; + tensor reshape_23_shape_0 = const()[name = tensor("reshape_23_shape_0"), val = tensor([-1])]; + tensor reshape_23 = reshape(shape = reshape_23_shape_0, x = cache2)[name = tensor("reshape_23")]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4_validate_indices_0 = const()[name = tensor("scatter_4_validate_indices_0"), val = tensor(false)]; + tensor scatter_4 = scatter(axis = scatter_4_axis_0, data = reshape_23, indices = reshape_21, mode = scatter_4_mode_0, updates = reshape_22, validate_indices = scatter_4_validate_indices_0)[name = tensor("scatter_4")]; + tensor reshape_24 = reshape(shape = shape_14, x = scatter_4)[name = tensor("reshape_24")]; + tensor var_1165_begin_0 = const()[name = tensor("op_1165_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1165_end_0 = const()[name = tensor("op_1165_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1165_end_mask_0 = const()[name = tensor("op_1165_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1165_squeeze_mask_0 = const()[name = tensor("op_1165_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1165 = slice_by_index(begin = var_1165_begin_0, end = var_1165_end_0, end_mask = var_1165_end_mask_0, squeeze_mask = var_1165_squeeze_mask_0, x = reshape_24)[name = tensor("op_1165")]; + tensor var_1167_axis_0 = const()[name = tensor("op_1167_axis_0"), val = tensor(1)]; + tensor var_1167_mode_0 = const()[name = tensor("op_1167_mode_0"), val = tensor("update")]; + tensor var_1167_validate_indices_0 = const()[name = tensor("op_1167_validate_indices_0"), val = tensor(false)]; + tensor var_1167 = scatter_along_axis(axis = var_1167_axis_0, data = var_1165, indices = write_indices_5, mode = var_1167_mode_0, updates = v_5, validate_indices = var_1167_validate_indices_0)[name = tensor("op_1167")]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_18 = const()[name = tensor("concat_18"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_15 = const()[name = tensor("shape_15"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_5 = const()[name = tensor("reduce_prod_5"), val = tensor(1048576)]; + tensor range_1d_5_start_0 = const()[name = tensor("range_1d_5_start_0"), val = tensor(0)]; + tensor range_1d_5_step_0 = const()[name = tensor("range_1d_5_step_0"), val = tensor(1)]; + tensor range_1d_5 = range_1d(end = reduce_prod_5, start = range_1d_5_start_0, step = range_1d_5_step_0)[name = tensor("range_1d_5")]; + tensor reshape_25 = reshape(shape = shape_15, x = range_1d_5)[name = tensor("reshape_25")]; + tensor slice_by_index_5 = slice_by_index(begin = concat_17, begin_mask = new_cache_5_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = new_cache_5_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_2_stride_0, x = reshape_25)[name = tensor("slice_by_index_5")]; + tensor reshape_26_shape_0 = const()[name = tensor("reshape_26_shape_0"), val = tensor([-1])]; + tensor reshape_26 = reshape(shape = reshape_26_shape_0, x = slice_by_index_5)[name = tensor("reshape_26")]; + tensor reshape_27_shape_0 = const()[name = tensor("reshape_27_shape_0"), val = tensor([-1])]; + tensor reshape_27 = reshape(shape = reshape_27_shape_0, x = var_1167)[name = tensor("reshape_27")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([-1])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = reshape_24)[name = tensor("reshape_28")]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5_validate_indices_0 = const()[name = tensor("scatter_5_validate_indices_0"), val = tensor(false)]; + tensor scatter_5 = scatter(axis = scatter_5_axis_0, data = reshape_28, indices = reshape_26, mode = scatter_5_mode_0, updates = reshape_27, validate_indices = scatter_5_validate_indices_0)[name = tensor("scatter_5")]; + tensor new_cache_5_internal_tensor_assign_2 = reshape(shape = shape_15, x = scatter_5)[name = tensor("reshape_29")]; + tensor keys_13_begin_0 = const()[name = tensor("keys_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_13_end_0 = const()[name = tensor("keys_13_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_13_end_mask_0 = const()[name = tensor("keys_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_13_squeeze_mask_0 = const()[name = tensor("keys_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_13 = slice_by_index(begin = keys_13_begin_0, end = keys_13_end_0, end_mask = keys_13_end_mask_0, squeeze_mask = keys_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("keys_13")]; + tensor values_13_begin_0 = const()[name = tensor("values_13_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_13_end_0 = const()[name = tensor("values_13_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_13_end_mask_0 = const()[name = tensor("values_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_13_squeeze_mask_0 = const()[name = tensor("values_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_13 = slice_by_index(begin = values_13_begin_0, end = values_13_end_0, end_mask = values_13_end_mask_0, squeeze_mask = values_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("values_13")]; + tensor var_1179 = not_equal(x = keys_13, y = keys_13)[name = tensor("op_1179")]; + tensor keys_15 = select(a = var_347, b = keys_13, cond = var_1179)[name = tensor("keys_15")]; + tensor var_1187 = not_equal(x = values_13, y = values_13)[name = tensor("op_1187")]; + tensor values_15 = select(a = var_347, b = values_13, cond = var_1187)[name = tensor("values_15")]; + tensor var_1211 = const()[name = tensor("op_1211"), val = tensor([0, 2, 1, 3])]; + tensor var_1224 = const()[name = tensor("op_1224"), val = tensor([1, 1, 1])]; + tensor var_1225 = reshape(shape = var_1224, x = position2)[name = tensor("op_1225")]; + tensor var_1242 = const()[name = tensor("op_1242"), val = tensor(0x1p+0)]; + tensor valid_len_5 = add(x = var_1225, y = var_1242)[name = tensor("valid_len_5")]; + tensor valid_mask_5 = less(x = k_positions_1_promoted, y = valid_len_5)[name = tensor("valid_mask_5")]; + tensor causal_mask_5 = less_equal(x = k_positions_1_promoted, y = var_1225)[name = tensor("causal_mask_5")]; + tensor attn_mask_9 = logical_and(x = valid_mask_5, y = causal_mask_5)[name = tensor("attn_mask_9")]; + tensor attn_mask_11_axes_0 = const()[name = tensor("attn_mask_11_axes_0"), val = tensor([1])]; + tensor attn_mask_11 = expand_dims(axes = attn_mask_11_axes_0, x = attn_mask_9)[name = tensor("attn_mask_11")]; + tensor var_1254 = const()[name = tensor("op_1254"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1260_transpose_x_0 = const()[name = tensor("op_1260_transpose_x_0"), val = tensor(false)]; + tensor var_1260_transpose_y_0 = const()[name = tensor("op_1260_transpose_y_0"), val = tensor(false)]; + tensor transpose_19_perm_0 = const()[name = tensor("transpose_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_20_perm_0 = const()[name = tensor("transpose_20_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_20 = transpose(perm = transpose_20_perm_0, x = keys_15)[name = tensor("transpose_34")]; + tensor transpose_19 = transpose(perm = transpose_19_perm_0, x = q_15)[name = tensor("transpose_35")]; + tensor var_1260 = matmul(transpose_x = var_1260_transpose_x_0, transpose_y = var_1260_transpose_y_0, x = transpose_19, y = transpose_20)[name = tensor("op_1260")]; + tensor attn_weights_13 = mul(x = var_1260, y = var_1254)[name = tensor("attn_weights_13")]; + tensor var_1262 = logical_not(x = attn_mask_11)[name = tensor("op_1262")]; + tensor var_1263 = const()[name = tensor("op_1263"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_15 = select(a = var_1263, b = attn_weights_13, cond = var_1262)[name = tensor("attn_weights_15")]; + tensor var_1265 = const()[name = tensor("op_1265"), val = tensor(-1)]; + tensor attn_weights_17 = softmax(axis = var_1265, x = attn_weights_15)[name = tensor("attn_weights_17")]; + tensor attn_output_5_transpose_x_0 = const()[name = tensor("attn_output_5_transpose_x_0"), val = tensor(false)]; + tensor attn_output_5_transpose_y_0 = const()[name = tensor("attn_output_5_transpose_y_0"), val = tensor(false)]; + tensor values_17 = transpose(perm = var_1211, x = values_15)[name = tensor("transpose_36")]; + tensor attn_output_5 = matmul(transpose_x = attn_output_5_transpose_x_0, transpose_y = attn_output_5_transpose_y_0, x = attn_weights_17, y = values_17)[name = tensor("attn_output_5")]; + tensor var_1273 = const()[name = tensor("op_1273"), val = tensor([0, 2, 1, 3])]; + tensor var_1276 = const()[name = tensor("op_1276"), val = tensor([1, 1, 1024])]; + tensor var_1274 = transpose(perm = var_1273, x = attn_output_5)[name = tensor("transpose_33")]; + tensor input_23 = reshape(shape = var_1276, x = var_1274)[name = tensor("input_23")]; + tensor attn_out_5 = linear(bias = linear_1_bias_0, weight = attn2_out_proj_weight, x = input_23)[name = tensor("linear_9")]; + tensor var_1282 = const()[name = tensor("op_1282"), val = tensor(0x1p+0)]; + tensor var_1283 = add(x = position2, y = var_1282)[name = tensor("op_1283")]; + tensor input_25 = add(x = input_21, y = attn_out_5)[name = tensor("input_25")]; + tensor var_1287 = const()[name = tensor("op_1287"), val = tensor(0x1.4f8b58p-17)]; + tensor input_27_axes_0 = const()[name = tensor("input_27_axes_0"), val = tensor([-1])]; + tensor input_27 = layer_norm(axes = input_27_axes_0, beta = norm2_2_bias, epsilon = var_1287, gamma = norm2_2_weight, x = input_25)[name = tensor("input_27")]; + tensor var_1295 = linear(bias = linear_2_bias_0, weight = linear2_1_weight, x = input_27)[name = tensor("linear_10")]; + tensor input_29_mode_0 = const()[name = tensor("input_29_mode_0"), val = tensor("EXACT")]; + tensor input_29 = gelu(mode = input_29_mode_0, x = var_1295)[name = tensor("input_29")]; + tensor ffn_out_5 = linear(bias = linear_1_bias_0, weight = linear2_2_weight, x = input_29)[name = tensor("linear_11")]; + tensor input_31 = add(x = input_25, y = ffn_out_5)[name = tensor("input_31")]; + tensor var_1304 = const()[name = tensor("op_1304"), val = tensor(0x1.4f8b58p-17)]; + tensor x_7_axes_0 = const()[name = tensor("x_7_axes_0"), val = tensor([-1])]; + tensor x_7 = layer_norm(axes = x_7_axes_0, beta = norm3_1_bias, epsilon = var_1304, gamma = norm3_1_weight, x = input_31)[name = tensor("x_7")]; + tensor var_1336 = linear(bias = linear_0_bias_0, weight = attn3_in_proj_weight, x = x_7)[name = tensor("linear_12")]; + tensor var_1340 = const()[name = tensor("op_1340"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_7 = reshape(shape = var_1340, x = var_1336)[name = tensor("qkv_7")]; + tensor q_19_begin_0 = const()[name = tensor("q_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_19_end_0 = const()[name = tensor("q_19_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_19_end_mask_0 = const()[name = tensor("q_19_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_19_squeeze_mask_0 = const()[name = tensor("q_19_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_19 = slice_by_index(begin = q_19_begin_0, end = q_19_end_0, end_mask = q_19_end_mask_0, squeeze_mask = q_19_squeeze_mask_0, x = qkv_7)[name = tensor("q_19")]; + tensor k_13_begin_0 = const()[name = tensor("k_13_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_13_end_0 = const()[name = tensor("k_13_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_13_end_mask_0 = const()[name = tensor("k_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_13_squeeze_mask_0 = const()[name = tensor("k_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_13 = slice_by_index(begin = k_13_begin_0, end = k_13_end_0, end_mask = k_13_end_mask_0, squeeze_mask = k_13_squeeze_mask_0, x = qkv_7)[name = tensor("k_13")]; + tensor v_7_begin_0 = const()[name = tensor("v_7_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_7_end_0 = const()[name = tensor("v_7_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_7_end_mask_0 = const()[name = tensor("v_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_7_squeeze_mask_0 = const()[name = tensor("v_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_7 = slice_by_index(begin = v_7_begin_0, end = v_7_end_0, end_mask = v_7_end_mask_0, squeeze_mask = v_7_squeeze_mask_0, x = qkv_7)[name = tensor("v_7")]; + tensor freqs_7 = const()[name = tensor("freqs_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266466944)))]; + tensor var_1444 = const()[name = tensor("op_1444"), val = tensor([1, 1, 1, 1])]; + tensor ts_23 = reshape(shape = var_1444, x = position3)[name = tensor("ts_23")]; + tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_7 = reshape(shape = var_1448, x = q_19)[name = tensor("q_complex_7")]; + tensor var_1452 = const()[name = tensor("op_1452"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_7 = reshape(shape = var_1452, x = k_13)[name = tensor("k_complex_7")]; + tensor var_1456_begin_0 = const()[name = tensor("op_1456_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1456_end_0 = const()[name = tensor("op_1456_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1456_end_mask_0 = const()[name = tensor("op_1456_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1456_squeeze_mask_0 = const()[name = tensor("op_1456_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1456 = slice_by_index(begin = var_1456_begin_0, end = var_1456_end_0, end_mask = var_1456_end_mask_0, squeeze_mask = var_1456_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1456")]; + tensor var_1464_begin_0 = const()[name = tensor("op_1464_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1464_end_0 = const()[name = tensor("op_1464_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1464_end_mask_0 = const()[name = tensor("op_1464_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1464_squeeze_mask_0 = const()[name = tensor("op_1464_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1464 = slice_by_index(begin = var_1464_begin_0, end = var_1464_end_0, end_mask = var_1464_end_mask_0, squeeze_mask = var_1464_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1464")]; + tensor var_1472_begin_0 = const()[name = tensor("op_1472_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1472_end_0 = const()[name = tensor("op_1472_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1472_end_mask_0 = const()[name = tensor("op_1472_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1472_squeeze_mask_0 = const()[name = tensor("op_1472_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1472 = slice_by_index(begin = var_1472_begin_0, end = var_1472_end_0, end_mask = var_1472_end_mask_0, squeeze_mask = var_1472_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1472")]; + tensor var_1480_begin_0 = const()[name = tensor("op_1480_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1480_end_0 = const()[name = tensor("op_1480_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1480_end_mask_0 = const()[name = tensor("op_1480_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1480_squeeze_mask_0 = const()[name = tensor("op_1480_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1480 = slice_by_index(begin = var_1480_begin_0, end = var_1480_end_0, end_mask = var_1480_end_mask_0, squeeze_mask = var_1480_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1480")]; + tensor var_1486 = mul(x = freqs_7, y = ts_23)[name = tensor("op_1486")]; + tensor rotr_7 = cos(x = var_1486)[name = tensor("rotr_7")]; + tensor roti_7 = sin(x = var_1486)[name = tensor("roti_7")]; + tensor var_1490 = mul(x = var_1456, y = rotr_7)[name = tensor("op_1490")]; + tensor var_1491 = mul(x = var_1464, y = roti_7)[name = tensor("op_1491")]; + tensor qor_13 = sub(x = var_1490, y = var_1491)[name = tensor("qor_13")]; + tensor var_1494 = mul(x = var_1456, y = roti_7)[name = tensor("op_1494")]; + tensor var_1495 = mul(x = var_1464, y = rotr_7)[name = tensor("op_1495")]; + tensor qoi_13 = add(x = var_1494, y = var_1495)[name = tensor("qoi_13")]; + tensor var_1498 = mul(x = var_1472, y = rotr_7)[name = tensor("op_1498")]; + tensor var_1499 = mul(x = var_1480, y = roti_7)[name = tensor("op_1499")]; + tensor kor_13 = sub(x = var_1498, y = var_1499)[name = tensor("kor_13")]; + tensor var_1502 = mul(x = var_1472, y = roti_7)[name = tensor("op_1502")]; + tensor var_1503 = mul(x = var_1480, y = rotr_7)[name = tensor("op_1503")]; + tensor koi_13 = add(x = var_1502, y = var_1503)[name = tensor("koi_13")]; + tensor qo_7_axis_0 = const()[name = tensor("qo_7_axis_0"), val = tensor(-1)]; + tensor qo_7 = stack(axis = qo_7_axis_0, values = (qor_13, qoi_13))[name = tensor("qo_7")]; + tensor ko_7_axis_0 = const()[name = tensor("ko_7_axis_0"), val = tensor(-1)]; + tensor ko_7 = stack(axis = ko_7_axis_0, values = (kor_13, koi_13))[name = tensor("ko_7")]; + tensor var_1532 = const()[name = tensor("op_1532"), val = tensor([1, 1, 16, 64])]; + tensor q_21 = reshape(shape = var_1532, x = qo_7)[name = tensor("q_21")]; + tensor var_1534 = const()[name = tensor("op_1534"), val = tensor([1, 1, 16, 64])]; + tensor k_15 = reshape(shape = var_1534, x = ko_7)[name = tensor("k_15")]; + tensor _inversed_1556_y_0 = const()[name = tensor("_inversed_1556_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1556 = mul(x = ts_23, y = _inversed_1556_y_0)[name = tensor("_inversed_1556")]; + tensor var_1557 = floor(x = _inversed_1556)[name = tensor("op_1557")]; + tensor var_1558 = const()[name = tensor("op_1558"), val = tensor(0x1p+9)]; + tensor var_1559 = mul(x = var_1557, y = var_1558)[name = tensor("op_1559")]; + tensor write_indices_float_15 = sub(x = ts_23, y = var_1559)[name = tensor("write_indices_float_15")]; + tensor var_1566_dtype_0 = const()[name = tensor("op_1566_dtype_0"), val = tensor("int32")]; + tensor write_indices_7_reps_0 = const()[name = tensor("write_indices_7_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1566 = cast(dtype = var_1566_dtype_0, x = write_indices_float_15)[name = tensor("cast_101")]; + tensor write_indices_7 = tile(reps = write_indices_7_reps_0, x = var_1566)[name = tensor("write_indices_7")]; + tensor var_1574_begin_0 = const()[name = tensor("op_1574_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1574_end_0 = const()[name = tensor("op_1574_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1574_end_mask_0 = const()[name = tensor("op_1574_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1574_squeeze_mask_0 = const()[name = tensor("op_1574_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1574 = slice_by_index(begin = var_1574_begin_0, end = var_1574_end_0, end_mask = var_1574_end_mask_0, squeeze_mask = var_1574_squeeze_mask_0, x = cache3)[name = tensor("op_1574")]; + tensor var_1576_axis_0 = const()[name = tensor("op_1576_axis_0"), val = tensor(1)]; + tensor var_1576_mode_0 = const()[name = tensor("op_1576_mode_0"), val = tensor("update")]; + tensor var_1576_validate_indices_0 = const()[name = tensor("op_1576_validate_indices_0"), val = tensor(false)]; + tensor var_1576 = scatter_along_axis(axis = var_1576_axis_0, data = var_1574, indices = write_indices_7, mode = var_1576_mode_0, updates = k_15, validate_indices = var_1576_validate_indices_0)[name = tensor("op_1576")]; + tensor concat_22 = const()[name = tensor("concat_22"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_23 = const()[name = tensor("concat_23"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_16 = const()[name = tensor("shape_16"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_6 = const()[name = tensor("reduce_prod_6"), val = tensor(1048576)]; + tensor range_1d_6_start_0 = const()[name = tensor("range_1d_6_start_0"), val = tensor(0)]; + tensor range_1d_6_step_0 = const()[name = tensor("range_1d_6_step_0"), val = tensor(1)]; + tensor range_1d_6 = range_1d(end = reduce_prod_6, start = range_1d_6_start_0, step = range_1d_6_step_0)[name = tensor("range_1d_6")]; + tensor reshape_30 = reshape(shape = shape_16, x = range_1d_6)[name = tensor("reshape_30")]; + tensor slice_by_index_6 = slice_by_index(begin = concat_22, begin_mask = new_cache_7_internal_tensor_assign_1_begin_mask_0, end = concat_23, end_mask = new_cache_7_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_1_stride_0, x = reshape_30)[name = tensor("slice_by_index_6")]; + tensor reshape_31_shape_0 = const()[name = tensor("reshape_31_shape_0"), val = tensor([-1])]; + tensor reshape_31 = reshape(shape = reshape_31_shape_0, x = slice_by_index_6)[name = tensor("reshape_31")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([-1])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = var_1576)[name = tensor("reshape_32")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([-1])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = cache3)[name = tensor("reshape_33")]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6_validate_indices_0 = const()[name = tensor("scatter_6_validate_indices_0"), val = tensor(false)]; + tensor scatter_6 = scatter(axis = scatter_6_axis_0, data = reshape_33, indices = reshape_31, mode = scatter_6_mode_0, updates = reshape_32, validate_indices = scatter_6_validate_indices_0)[name = tensor("scatter_6")]; + tensor reshape_34 = reshape(shape = shape_16, x = scatter_6)[name = tensor("reshape_34")]; + tensor var_1584_begin_0 = const()[name = tensor("op_1584_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1584_end_0 = const()[name = tensor("op_1584_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1584_end_mask_0 = const()[name = tensor("op_1584_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1584_squeeze_mask_0 = const()[name = tensor("op_1584_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1584 = slice_by_index(begin = var_1584_begin_0, end = var_1584_end_0, end_mask = var_1584_end_mask_0, squeeze_mask = var_1584_squeeze_mask_0, x = reshape_34)[name = tensor("op_1584")]; + tensor var_1586_axis_0 = const()[name = tensor("op_1586_axis_0"), val = tensor(1)]; + tensor var_1586_mode_0 = const()[name = tensor("op_1586_mode_0"), val = tensor("update")]; + tensor var_1586_validate_indices_0 = const()[name = tensor("op_1586_validate_indices_0"), val = tensor(false)]; + tensor var_1586 = scatter_along_axis(axis = var_1586_axis_0, data = var_1584, indices = write_indices_7, mode = var_1586_mode_0, updates = v_7, validate_indices = var_1586_validate_indices_0)[name = tensor("op_1586")]; + tensor concat_24 = const()[name = tensor("concat_24"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_25 = const()[name = tensor("concat_25"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_17 = const()[name = tensor("shape_17"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_7 = const()[name = tensor("reduce_prod_7"), val = tensor(1048576)]; + tensor range_1d_7_start_0 = const()[name = tensor("range_1d_7_start_0"), val = tensor(0)]; + tensor range_1d_7_step_0 = const()[name = tensor("range_1d_7_step_0"), val = tensor(1)]; + tensor range_1d_7 = range_1d(end = reduce_prod_7, start = range_1d_7_start_0, step = range_1d_7_step_0)[name = tensor("range_1d_7")]; + tensor reshape_35 = reshape(shape = shape_17, x = range_1d_7)[name = tensor("reshape_35")]; + tensor slice_by_index_7 = slice_by_index(begin = concat_24, begin_mask = new_cache_7_internal_tensor_assign_2_begin_mask_0, end = concat_25, end_mask = new_cache_7_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_2_stride_0, x = reshape_35)[name = tensor("slice_by_index_7")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([-1])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = slice_by_index_7)[name = tensor("reshape_36")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([-1])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = var_1586)[name = tensor("reshape_37")]; + tensor reshape_38_shape_0 = const()[name = tensor("reshape_38_shape_0"), val = tensor([-1])]; + tensor reshape_38 = reshape(shape = reshape_38_shape_0, x = reshape_34)[name = tensor("reshape_38")]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7_validate_indices_0 = const()[name = tensor("scatter_7_validate_indices_0"), val = tensor(false)]; + tensor scatter_7 = scatter(axis = scatter_7_axis_0, data = reshape_38, indices = reshape_36, mode = scatter_7_mode_0, updates = reshape_37, validate_indices = scatter_7_validate_indices_0)[name = tensor("scatter_7")]; + tensor new_cache_7_internal_tensor_assign_2 = reshape(shape = shape_17, x = scatter_7)[name = tensor("reshape_39")]; + tensor keys_19_begin_0 = const()[name = tensor("keys_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_19_end_0 = const()[name = tensor("keys_19_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_19_end_mask_0 = const()[name = tensor("keys_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_19_squeeze_mask_0 = const()[name = tensor("keys_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_19 = slice_by_index(begin = keys_19_begin_0, end = keys_19_end_0, end_mask = keys_19_end_mask_0, squeeze_mask = keys_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("keys_19")]; + tensor values_19_begin_0 = const()[name = tensor("values_19_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_19_end_0 = const()[name = tensor("values_19_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_19_end_mask_0 = const()[name = tensor("values_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_19_squeeze_mask_0 = const()[name = tensor("values_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_19 = slice_by_index(begin = values_19_begin_0, end = values_19_end_0, end_mask = values_19_end_mask_0, squeeze_mask = values_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("values_19")]; + tensor var_1598 = not_equal(x = keys_19, y = keys_19)[name = tensor("op_1598")]; + tensor keys_21 = select(a = var_347, b = keys_19, cond = var_1598)[name = tensor("keys_21")]; + tensor var_1606 = not_equal(x = values_19, y = values_19)[name = tensor("op_1606")]; + tensor values_21 = select(a = var_347, b = values_19, cond = var_1606)[name = tensor("values_21")]; + tensor var_1630 = const()[name = tensor("op_1630"), val = tensor([0, 2, 1, 3])]; + tensor var_1643 = const()[name = tensor("op_1643"), val = tensor([1, 1, 1])]; + tensor var_1644 = reshape(shape = var_1643, x = position3)[name = tensor("op_1644")]; + tensor var_1661 = const()[name = tensor("op_1661"), val = tensor(0x1p+0)]; + tensor valid_len_7 = add(x = var_1644, y = var_1661)[name = tensor("valid_len_7")]; + tensor valid_mask_7 = less(x = k_positions_1_promoted, y = valid_len_7)[name = tensor("valid_mask_7")]; + tensor causal_mask_7 = less_equal(x = k_positions_1_promoted, y = var_1644)[name = tensor("causal_mask_7")]; + tensor attn_mask_13 = logical_and(x = valid_mask_7, y = causal_mask_7)[name = tensor("attn_mask_13")]; + tensor attn_mask_15_axes_0 = const()[name = tensor("attn_mask_15_axes_0"), val = tensor([1])]; + tensor attn_mask_15 = expand_dims(axes = attn_mask_15_axes_0, x = attn_mask_13)[name = tensor("attn_mask_15")]; + tensor var_1673 = const()[name = tensor("op_1673"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1679_transpose_x_0 = const()[name = tensor("op_1679_transpose_x_0"), val = tensor(false)]; + tensor var_1679_transpose_y_0 = const()[name = tensor("op_1679_transpose_y_0"), val = tensor(false)]; + tensor transpose_21_perm_0 = const()[name = tensor("transpose_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_22_perm_0 = const()[name = tensor("transpose_22_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_22 = transpose(perm = transpose_22_perm_0, x = keys_21)[name = tensor("transpose_30")]; + tensor transpose_21 = transpose(perm = transpose_21_perm_0, x = q_21)[name = tensor("transpose_31")]; + tensor var_1679 = matmul(transpose_x = var_1679_transpose_x_0, transpose_y = var_1679_transpose_y_0, x = transpose_21, y = transpose_22)[name = tensor("op_1679")]; + tensor attn_weights_19 = mul(x = var_1679, y = var_1673)[name = tensor("attn_weights_19")]; + tensor var_1681 = logical_not(x = attn_mask_15)[name = tensor("op_1681")]; + tensor var_1682 = const()[name = tensor("op_1682"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_21 = select(a = var_1682, b = attn_weights_19, cond = var_1681)[name = tensor("attn_weights_21")]; + tensor var_1684 = const()[name = tensor("op_1684"), val = tensor(-1)]; + tensor attn_weights_23 = softmax(axis = var_1684, x = attn_weights_21)[name = tensor("attn_weights_23")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor values_23 = transpose(perm = var_1630, x = values_21)[name = tensor("transpose_32")]; + tensor attn_output_7 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = attn_weights_23, y = values_23)[name = tensor("attn_output_7")]; + tensor var_1692 = const()[name = tensor("op_1692"), val = tensor([0, 2, 1, 3])]; + tensor var_1695 = const()[name = tensor("op_1695"), val = tensor([1, 1, 1024])]; + tensor var_1693 = transpose(perm = var_1692, x = attn_output_7)[name = tensor("transpose_29")]; + tensor input_33 = reshape(shape = var_1695, x = var_1693)[name = tensor("input_33")]; + tensor attn_out_7 = linear(bias = linear_1_bias_0, weight = attn3_out_proj_weight, x = input_33)[name = tensor("linear_13")]; + tensor var_1701 = const()[name = tensor("op_1701"), val = tensor(0x1p+0)]; + tensor var_1702 = add(x = position3, y = var_1701)[name = tensor("op_1702")]; + tensor input_35 = add(x = input_31, y = attn_out_7)[name = tensor("input_35")]; + tensor var_1706 = const()[name = tensor("op_1706"), val = tensor(0x1.4f8b58p-17)]; + tensor input_37_axes_0 = const()[name = tensor("input_37_axes_0"), val = tensor([-1])]; + tensor input_37 = layer_norm(axes = input_37_axes_0, beta = norm3_2_bias, epsilon = var_1706, gamma = norm3_2_weight, x = input_35)[name = tensor("input_37")]; + tensor var_1714 = linear(bias = linear_2_bias_0, weight = linear3_1_weight, x = input_37)[name = tensor("linear_14")]; + tensor input_39_mode_0 = const()[name = tensor("input_39_mode_0"), val = tensor("EXACT")]; + tensor input_39 = gelu(mode = input_39_mode_0, x = var_1714)[name = tensor("input_39")]; + tensor ffn_out_7 = linear(bias = linear_1_bias_0, weight = linear3_2_weight, x = input_39)[name = tensor("linear_15")]; + tensor input_41 = add(x = input_35, y = ffn_out_7)[name = tensor("input_41")]; + tensor var_1723 = const()[name = tensor("op_1723"), val = tensor(0x1.4f8b58p-17)]; + tensor x_9_axes_0 = const()[name = tensor("x_9_axes_0"), val = tensor([-1])]; + tensor x_9 = layer_norm(axes = x_9_axes_0, beta = norm4_1_bias, epsilon = var_1723, gamma = norm4_1_weight, x = input_41)[name = tensor("x_9")]; + tensor var_1755 = linear(bias = linear_0_bias_0, weight = attn4_in_proj_weight, x = x_9)[name = tensor("linear_16")]; + tensor var_1759 = const()[name = tensor("op_1759"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_9 = reshape(shape = var_1759, x = var_1755)[name = tensor("qkv_9")]; + tensor q_25_begin_0 = const()[name = tensor("q_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_25_end_0 = const()[name = tensor("q_25_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_25_end_mask_0 = const()[name = tensor("q_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_25_squeeze_mask_0 = const()[name = tensor("q_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_25 = slice_by_index(begin = q_25_begin_0, end = q_25_end_0, end_mask = q_25_end_mask_0, squeeze_mask = q_25_squeeze_mask_0, x = qkv_9)[name = tensor("q_25")]; + tensor k_17_begin_0 = const()[name = tensor("k_17_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_17_end_0 = const()[name = tensor("k_17_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_17_end_mask_0 = const()[name = tensor("k_17_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_17_squeeze_mask_0 = const()[name = tensor("k_17_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_17 = slice_by_index(begin = k_17_begin_0, end = k_17_end_0, end_mask = k_17_end_mask_0, squeeze_mask = k_17_squeeze_mask_0, x = qkv_9)[name = tensor("k_17")]; + tensor v_9_begin_0 = const()[name = tensor("v_9_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_9_end_0 = const()[name = tensor("v_9_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_9_end_mask_0 = const()[name = tensor("v_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_9_squeeze_mask_0 = const()[name = tensor("v_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_9 = slice_by_index(begin = v_9_begin_0, end = v_9_end_0, end_mask = v_9_end_mask_0, squeeze_mask = v_9_squeeze_mask_0, x = qkv_9)[name = tensor("v_9")]; + tensor freqs_9 = const()[name = tensor("freqs_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266467136)))]; + tensor var_1863 = const()[name = tensor("op_1863"), val = tensor([1, 1, 1, 1])]; + tensor ts_29 = reshape(shape = var_1863, x = position4)[name = tensor("ts_29")]; + tensor var_1867 = const()[name = tensor("op_1867"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_9 = reshape(shape = var_1867, x = q_25)[name = tensor("q_complex_9")]; + tensor var_1871 = const()[name = tensor("op_1871"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_9 = reshape(shape = var_1871, x = k_17)[name = tensor("k_complex_9")]; + tensor var_1875_begin_0 = const()[name = tensor("op_1875_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1875_end_0 = const()[name = tensor("op_1875_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1875_end_mask_0 = const()[name = tensor("op_1875_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1875_squeeze_mask_0 = const()[name = tensor("op_1875_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1875 = slice_by_index(begin = var_1875_begin_0, end = var_1875_end_0, end_mask = var_1875_end_mask_0, squeeze_mask = var_1875_squeeze_mask_0, x = q_complex_9)[name = tensor("op_1875")]; + tensor var_1883_begin_0 = const()[name = tensor("op_1883_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1883_end_0 = const()[name = tensor("op_1883_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1883_end_mask_0 = const()[name = tensor("op_1883_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1883_squeeze_mask_0 = const()[name = tensor("op_1883_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1883 = slice_by_index(begin = var_1883_begin_0, end = var_1883_end_0, end_mask = var_1883_end_mask_0, squeeze_mask = var_1883_squeeze_mask_0, x = q_complex_9)[name = tensor("op_1883")]; + tensor var_1891_begin_0 = const()[name = tensor("op_1891_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1891_end_0 = const()[name = tensor("op_1891_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1891_end_mask_0 = const()[name = tensor("op_1891_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1891_squeeze_mask_0 = const()[name = tensor("op_1891_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1891 = slice_by_index(begin = var_1891_begin_0, end = var_1891_end_0, end_mask = var_1891_end_mask_0, squeeze_mask = var_1891_squeeze_mask_0, x = k_complex_9)[name = tensor("op_1891")]; + tensor var_1899_begin_0 = const()[name = tensor("op_1899_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1899_end_0 = const()[name = tensor("op_1899_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1899_end_mask_0 = const()[name = tensor("op_1899_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1899_squeeze_mask_0 = const()[name = tensor("op_1899_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1899 = slice_by_index(begin = var_1899_begin_0, end = var_1899_end_0, end_mask = var_1899_end_mask_0, squeeze_mask = var_1899_squeeze_mask_0, x = k_complex_9)[name = tensor("op_1899")]; + tensor var_1905 = mul(x = freqs_9, y = ts_29)[name = tensor("op_1905")]; + tensor rotr_9 = cos(x = var_1905)[name = tensor("rotr_9")]; + tensor roti_9 = sin(x = var_1905)[name = tensor("roti_9")]; + tensor var_1909 = mul(x = var_1875, y = rotr_9)[name = tensor("op_1909")]; + tensor var_1910 = mul(x = var_1883, y = roti_9)[name = tensor("op_1910")]; + tensor qor_17 = sub(x = var_1909, y = var_1910)[name = tensor("qor_17")]; + tensor var_1913 = mul(x = var_1875, y = roti_9)[name = tensor("op_1913")]; + tensor var_1914 = mul(x = var_1883, y = rotr_9)[name = tensor("op_1914")]; + tensor qoi_17 = add(x = var_1913, y = var_1914)[name = tensor("qoi_17")]; + tensor var_1917 = mul(x = var_1891, y = rotr_9)[name = tensor("op_1917")]; + tensor var_1918 = mul(x = var_1899, y = roti_9)[name = tensor("op_1918")]; + tensor kor_17 = sub(x = var_1917, y = var_1918)[name = tensor("kor_17")]; + tensor var_1921 = mul(x = var_1891, y = roti_9)[name = tensor("op_1921")]; + tensor var_1922 = mul(x = var_1899, y = rotr_9)[name = tensor("op_1922")]; + tensor koi_17 = add(x = var_1921, y = var_1922)[name = tensor("koi_17")]; + tensor qo_9_axis_0 = const()[name = tensor("qo_9_axis_0"), val = tensor(-1)]; + tensor qo_9 = stack(axis = qo_9_axis_0, values = (qor_17, qoi_17))[name = tensor("qo_9")]; + tensor ko_9_axis_0 = const()[name = tensor("ko_9_axis_0"), val = tensor(-1)]; + tensor ko_9 = stack(axis = ko_9_axis_0, values = (kor_17, koi_17))[name = tensor("ko_9")]; + tensor var_1951 = const()[name = tensor("op_1951"), val = tensor([1, 1, 16, 64])]; + tensor q_27 = reshape(shape = var_1951, x = qo_9)[name = tensor("q_27")]; + tensor var_1953 = const()[name = tensor("op_1953"), val = tensor([1, 1, 16, 64])]; + tensor k_19 = reshape(shape = var_1953, x = ko_9)[name = tensor("k_19")]; + tensor _inversed_1975_y_0 = const()[name = tensor("_inversed_1975_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1975 = mul(x = ts_29, y = _inversed_1975_y_0)[name = tensor("_inversed_1975")]; + tensor var_1976 = floor(x = _inversed_1975)[name = tensor("op_1976")]; + tensor var_1977 = const()[name = tensor("op_1977"), val = tensor(0x1p+9)]; + tensor var_1978 = mul(x = var_1976, y = var_1977)[name = tensor("op_1978")]; + tensor write_indices_float_19 = sub(x = ts_29, y = var_1978)[name = tensor("write_indices_float_19")]; + tensor var_1985_dtype_0 = const()[name = tensor("op_1985_dtype_0"), val = tensor("int32")]; + tensor write_indices_9_reps_0 = const()[name = tensor("write_indices_9_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1985 = cast(dtype = var_1985_dtype_0, x = write_indices_float_19)[name = tensor("cast_100")]; + tensor write_indices_9 = tile(reps = write_indices_9_reps_0, x = var_1985)[name = tensor("write_indices_9")]; + tensor var_1993_begin_0 = const()[name = tensor("op_1993_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1993_end_0 = const()[name = tensor("op_1993_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1993_end_mask_0 = const()[name = tensor("op_1993_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1993_squeeze_mask_0 = const()[name = tensor("op_1993_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1993 = slice_by_index(begin = var_1993_begin_0, end = var_1993_end_0, end_mask = var_1993_end_mask_0, squeeze_mask = var_1993_squeeze_mask_0, x = cache4)[name = tensor("op_1993")]; + tensor var_1995_axis_0 = const()[name = tensor("op_1995_axis_0"), val = tensor(1)]; + tensor var_1995_mode_0 = const()[name = tensor("op_1995_mode_0"), val = tensor("update")]; + tensor var_1995_validate_indices_0 = const()[name = tensor("op_1995_validate_indices_0"), val = tensor(false)]; + tensor var_1995 = scatter_along_axis(axis = var_1995_axis_0, data = var_1993, indices = write_indices_9, mode = var_1995_mode_0, updates = k_19, validate_indices = var_1995_validate_indices_0)[name = tensor("op_1995")]; + tensor concat_29 = const()[name = tensor("concat_29"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_30 = const()[name = tensor("concat_30"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_18 = const()[name = tensor("shape_18"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_8 = const()[name = tensor("reduce_prod_8"), val = tensor(1048576)]; + tensor range_1d_8_start_0 = const()[name = tensor("range_1d_8_start_0"), val = tensor(0)]; + tensor range_1d_8_step_0 = const()[name = tensor("range_1d_8_step_0"), val = tensor(1)]; + tensor range_1d_8 = range_1d(end = reduce_prod_8, start = range_1d_8_start_0, step = range_1d_8_step_0)[name = tensor("range_1d_8")]; + tensor reshape_40 = reshape(shape = shape_18, x = range_1d_8)[name = tensor("reshape_40")]; + tensor slice_by_index_8 = slice_by_index(begin = concat_29, begin_mask = new_cache_9_internal_tensor_assign_1_begin_mask_0, end = concat_30, end_mask = new_cache_9_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_1_stride_0, x = reshape_40)[name = tensor("slice_by_index_8")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([-1])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = slice_by_index_8)[name = tensor("reshape_41")]; + tensor reshape_42_shape_0 = const()[name = tensor("reshape_42_shape_0"), val = tensor([-1])]; + tensor reshape_42 = reshape(shape = reshape_42_shape_0, x = var_1995)[name = tensor("reshape_42")]; + tensor reshape_43_shape_0 = const()[name = tensor("reshape_43_shape_0"), val = tensor([-1])]; + tensor reshape_43 = reshape(shape = reshape_43_shape_0, x = cache4)[name = tensor("reshape_43")]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8_validate_indices_0 = const()[name = tensor("scatter_8_validate_indices_0"), val = tensor(false)]; + tensor scatter_8 = scatter(axis = scatter_8_axis_0, data = reshape_43, indices = reshape_41, mode = scatter_8_mode_0, updates = reshape_42, validate_indices = scatter_8_validate_indices_0)[name = tensor("scatter_8")]; + tensor reshape_44 = reshape(shape = shape_18, x = scatter_8)[name = tensor("reshape_44")]; + tensor var_2003_begin_0 = const()[name = tensor("op_2003_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2003_end_0 = const()[name = tensor("op_2003_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2003_end_mask_0 = const()[name = tensor("op_2003_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2003_squeeze_mask_0 = const()[name = tensor("op_2003_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2003 = slice_by_index(begin = var_2003_begin_0, end = var_2003_end_0, end_mask = var_2003_end_mask_0, squeeze_mask = var_2003_squeeze_mask_0, x = reshape_44)[name = tensor("op_2003")]; + tensor var_2005_axis_0 = const()[name = tensor("op_2005_axis_0"), val = tensor(1)]; + tensor var_2005_mode_0 = const()[name = tensor("op_2005_mode_0"), val = tensor("update")]; + tensor var_2005_validate_indices_0 = const()[name = tensor("op_2005_validate_indices_0"), val = tensor(false)]; + tensor var_2005 = scatter_along_axis(axis = var_2005_axis_0, data = var_2003, indices = write_indices_9, mode = var_2005_mode_0, updates = v_9, validate_indices = var_2005_validate_indices_0)[name = tensor("op_2005")]; + tensor concat_31 = const()[name = tensor("concat_31"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_32 = const()[name = tensor("concat_32"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_19 = const()[name = tensor("shape_19"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_9 = const()[name = tensor("reduce_prod_9"), val = tensor(1048576)]; + tensor range_1d_9_start_0 = const()[name = tensor("range_1d_9_start_0"), val = tensor(0)]; + tensor range_1d_9_step_0 = const()[name = tensor("range_1d_9_step_0"), val = tensor(1)]; + tensor range_1d_9 = range_1d(end = reduce_prod_9, start = range_1d_9_start_0, step = range_1d_9_step_0)[name = tensor("range_1d_9")]; + tensor reshape_45 = reshape(shape = shape_19, x = range_1d_9)[name = tensor("reshape_45")]; + tensor slice_by_index_9 = slice_by_index(begin = concat_31, begin_mask = new_cache_9_internal_tensor_assign_2_begin_mask_0, end = concat_32, end_mask = new_cache_9_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_2_stride_0, x = reshape_45)[name = tensor("slice_by_index_9")]; + tensor reshape_46_shape_0 = const()[name = tensor("reshape_46_shape_0"), val = tensor([-1])]; + tensor reshape_46 = reshape(shape = reshape_46_shape_0, x = slice_by_index_9)[name = tensor("reshape_46")]; + tensor reshape_47_shape_0 = const()[name = tensor("reshape_47_shape_0"), val = tensor([-1])]; + tensor reshape_47 = reshape(shape = reshape_47_shape_0, x = var_2005)[name = tensor("reshape_47")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([-1])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = reshape_44)[name = tensor("reshape_48")]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9_validate_indices_0 = const()[name = tensor("scatter_9_validate_indices_0"), val = tensor(false)]; + tensor scatter_9 = scatter(axis = scatter_9_axis_0, data = reshape_48, indices = reshape_46, mode = scatter_9_mode_0, updates = reshape_47, validate_indices = scatter_9_validate_indices_0)[name = tensor("scatter_9")]; + tensor new_cache_9_internal_tensor_assign_2 = reshape(shape = shape_19, x = scatter_9)[name = tensor("reshape_49")]; + tensor keys_25_begin_0 = const()[name = tensor("keys_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_25_end_0 = const()[name = tensor("keys_25_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_25_end_mask_0 = const()[name = tensor("keys_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_25_squeeze_mask_0 = const()[name = tensor("keys_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_25 = slice_by_index(begin = keys_25_begin_0, end = keys_25_end_0, end_mask = keys_25_end_mask_0, squeeze_mask = keys_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("keys_25")]; + tensor values_25_begin_0 = const()[name = tensor("values_25_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_25_end_0 = const()[name = tensor("values_25_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_25_end_mask_0 = const()[name = tensor("values_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_25_squeeze_mask_0 = const()[name = tensor("values_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_25 = slice_by_index(begin = values_25_begin_0, end = values_25_end_0, end_mask = values_25_end_mask_0, squeeze_mask = values_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("values_25")]; + tensor var_2017 = not_equal(x = keys_25, y = keys_25)[name = tensor("op_2017")]; + tensor keys_27 = select(a = var_347, b = keys_25, cond = var_2017)[name = tensor("keys_27")]; + tensor var_2025 = not_equal(x = values_25, y = values_25)[name = tensor("op_2025")]; + tensor values_27 = select(a = var_347, b = values_25, cond = var_2025)[name = tensor("values_27")]; + tensor var_2049 = const()[name = tensor("op_2049"), val = tensor([0, 2, 1, 3])]; + tensor var_2062 = const()[name = tensor("op_2062"), val = tensor([1, 1, 1])]; + tensor var_2063 = reshape(shape = var_2062, x = position4)[name = tensor("op_2063")]; + tensor var_2080 = const()[name = tensor("op_2080"), val = tensor(0x1p+0)]; + tensor valid_len_9 = add(x = var_2063, y = var_2080)[name = tensor("valid_len_9")]; + tensor valid_mask_9 = less(x = k_positions_1_promoted, y = valid_len_9)[name = tensor("valid_mask_9")]; + tensor causal_mask_9 = less_equal(x = k_positions_1_promoted, y = var_2063)[name = tensor("causal_mask_9")]; + tensor attn_mask_17 = logical_and(x = valid_mask_9, y = causal_mask_9)[name = tensor("attn_mask_17")]; + tensor attn_mask_19_axes_0 = const()[name = tensor("attn_mask_19_axes_0"), val = tensor([1])]; + tensor attn_mask_19 = expand_dims(axes = attn_mask_19_axes_0, x = attn_mask_17)[name = tensor("attn_mask_19")]; + tensor var_2092 = const()[name = tensor("op_2092"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2098_transpose_x_0 = const()[name = tensor("op_2098_transpose_x_0"), val = tensor(false)]; + tensor var_2098_transpose_y_0 = const()[name = tensor("op_2098_transpose_y_0"), val = tensor(false)]; + tensor transpose_23_perm_0 = const()[name = tensor("transpose_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_24_perm_0 = const()[name = tensor("transpose_24_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_24 = transpose(perm = transpose_24_perm_0, x = keys_27)[name = tensor("transpose_26")]; + tensor transpose_23 = transpose(perm = transpose_23_perm_0, x = q_27)[name = tensor("transpose_27")]; + tensor var_2098 = matmul(transpose_x = var_2098_transpose_x_0, transpose_y = var_2098_transpose_y_0, x = transpose_23, y = transpose_24)[name = tensor("op_2098")]; + tensor attn_weights_25 = mul(x = var_2098, y = var_2092)[name = tensor("attn_weights_25")]; + tensor var_2100 = logical_not(x = attn_mask_19)[name = tensor("op_2100")]; + tensor var_2101 = const()[name = tensor("op_2101"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_27 = select(a = var_2101, b = attn_weights_25, cond = var_2100)[name = tensor("attn_weights_27")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor(-1)]; + tensor attn_weights_29 = softmax(axis = var_2103, x = attn_weights_27)[name = tensor("attn_weights_29")]; + tensor attn_output_9_transpose_x_0 = const()[name = tensor("attn_output_9_transpose_x_0"), val = tensor(false)]; + tensor attn_output_9_transpose_y_0 = const()[name = tensor("attn_output_9_transpose_y_0"), val = tensor(false)]; + tensor values_29 = transpose(perm = var_2049, x = values_27)[name = tensor("transpose_28")]; + tensor attn_output_9 = matmul(transpose_x = attn_output_9_transpose_x_0, transpose_y = attn_output_9_transpose_y_0, x = attn_weights_29, y = values_29)[name = tensor("attn_output_9")]; + tensor var_2111 = const()[name = tensor("op_2111"), val = tensor([0, 2, 1, 3])]; + tensor var_2114 = const()[name = tensor("op_2114"), val = tensor([1, 1, 1024])]; + tensor var_2112 = transpose(perm = var_2111, x = attn_output_9)[name = tensor("transpose_25")]; + tensor input_43 = reshape(shape = var_2114, x = var_2112)[name = tensor("input_43")]; + tensor attn_out_9 = linear(bias = linear_1_bias_0, weight = attn4_out_proj_weight, x = input_43)[name = tensor("linear_17")]; + tensor var_2120 = const()[name = tensor("op_2120"), val = tensor(0x1p+0)]; + tensor var_2121 = add(x = position4, y = var_2120)[name = tensor("op_2121")]; + tensor input_45 = add(x = input_41, y = attn_out_9)[name = tensor("input_45")]; + tensor var_2125 = const()[name = tensor("op_2125"), val = tensor(0x1.4f8b58p-17)]; + tensor input_47_axes_0 = const()[name = tensor("input_47_axes_0"), val = tensor([-1])]; + tensor input_47 = layer_norm(axes = input_47_axes_0, beta = norm4_2_bias, epsilon = var_2125, gamma = norm4_2_weight, x = input_45)[name = tensor("input_47")]; + tensor var_2133 = linear(bias = linear_2_bias_0, weight = linear4_1_weight, x = input_47)[name = tensor("linear_18")]; + tensor input_49_mode_0 = const()[name = tensor("input_49_mode_0"), val = tensor("EXACT")]; + tensor input_49 = gelu(mode = input_49_mode_0, x = var_2133)[name = tensor("input_49")]; + tensor ffn_out_9 = linear(bias = linear_1_bias_0, weight = linear4_2_weight, x = input_49)[name = tensor("linear_19")]; + tensor input_51 = add(x = input_45, y = ffn_out_9)[name = tensor("input_51")]; + tensor var_2142 = const()[name = tensor("op_2142"), val = tensor(0x1.4f8b58p-17)]; + tensor x_axes_0 = const()[name = tensor("x_axes_0"), val = tensor([-1])]; + tensor x = layer_norm(axes = x_axes_0, beta = norm5_1_bias, epsilon = var_2142, gamma = norm5_1_weight, x = input_51)[name = tensor("x")]; + tensor var_2163 = linear(bias = linear_0_bias_0, weight = attn5_in_proj_weight, x = x)[name = tensor("linear_20")]; + tensor var_2167 = const()[name = tensor("op_2167"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv = reshape(shape = var_2167, x = var_2163)[name = tensor("qkv")]; + tensor k_21_begin_0 = const()[name = tensor("k_21_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_21_end_0 = const()[name = tensor("k_21_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_21_end_mask_0 = const()[name = tensor("k_21_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_21_squeeze_mask_0 = const()[name = tensor("k_21_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_21 = slice_by_index(begin = k_21_begin_0, end = k_21_end_0, end_mask = k_21_end_mask_0, squeeze_mask = k_21_squeeze_mask_0, x = qkv)[name = tensor("k_21")]; + tensor v_begin_0 = const()[name = tensor("v_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_end_0 = const()[name = tensor("v_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_end_mask_0 = const()[name = tensor("v_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_squeeze_mask_0 = const()[name = tensor("v_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v = slice_by_index(begin = v_begin_0, end = v_end_0, end_mask = v_end_mask_0, squeeze_mask = v_squeeze_mask_0, x = qkv)[name = tensor("v")]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266467328)))]; + tensor var_2258 = const()[name = tensor("op_2258"), val = tensor([1, 1, 1, 1])]; + tensor ts = reshape(shape = var_2258, x = position5)[name = tensor("ts")]; + tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex = reshape(shape = var_2262, x = k_21)[name = tensor("k_complex")]; + tensor var_2266_begin_0 = const()[name = tensor("op_2266_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2266_end_0 = const()[name = tensor("op_2266_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2266_end_mask_0 = const()[name = tensor("op_2266_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2266_squeeze_mask_0 = const()[name = tensor("op_2266_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2266 = slice_by_index(begin = var_2266_begin_0, end = var_2266_end_0, end_mask = var_2266_end_mask_0, squeeze_mask = var_2266_squeeze_mask_0, x = k_complex)[name = tensor("op_2266")]; + tensor var_2274_begin_0 = const()[name = tensor("op_2274_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2274_end_0 = const()[name = tensor("op_2274_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2274_end_mask_0 = const()[name = tensor("op_2274_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2274_squeeze_mask_0 = const()[name = tensor("op_2274_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2274 = slice_by_index(begin = var_2274_begin_0, end = var_2274_end_0, end_mask = var_2274_end_mask_0, squeeze_mask = var_2274_squeeze_mask_0, x = k_complex)[name = tensor("op_2274")]; + tensor var_2280 = mul(x = freqs, y = ts)[name = tensor("op_2280")]; + tensor rotr = cos(x = var_2280)[name = tensor("rotr")]; + tensor roti = sin(x = var_2280)[name = tensor("roti")]; + tensor var_2284 = mul(x = var_2266, y = rotr)[name = tensor("op_2284")]; + tensor var_2285 = mul(x = var_2274, y = roti)[name = tensor("op_2285")]; + tensor kor_21 = sub(x = var_2284, y = var_2285)[name = tensor("kor_21")]; + tensor var_2288 = mul(x = var_2266, y = roti)[name = tensor("op_2288")]; + tensor var_2289 = mul(x = var_2274, y = rotr)[name = tensor("op_2289")]; + tensor koi_21 = add(x = var_2288, y = var_2289)[name = tensor("koi_21")]; + tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; + tensor ko = stack(axis = ko_axis_0, values = (kor_21, koi_21))[name = tensor("ko")]; + tensor var_2305 = const()[name = tensor("op_2305"), val = tensor([1, 1, 16, 64])]; + tensor k = reshape(shape = var_2305, x = ko)[name = tensor("k")]; + tensor _inversed_2327_y_0 = const()[name = tensor("_inversed_2327_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2327 = mul(x = ts, y = _inversed_2327_y_0)[name = tensor("_inversed_2327")]; + tensor var_2328 = floor(x = _inversed_2327)[name = tensor("op_2328")]; + tensor var_2329 = const()[name = tensor("op_2329"), val = tensor(0x1p+9)]; + tensor var_2330 = mul(x = var_2328, y = var_2329)[name = tensor("op_2330")]; + tensor write_indices_float = sub(x = ts, y = var_2330)[name = tensor("write_indices_float")]; + tensor var_2337_dtype_0 = const()[name = tensor("op_2337_dtype_0"), val = tensor("int32")]; + tensor write_indices_reps_0 = const()[name = tensor("write_indices_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2337 = cast(dtype = var_2337_dtype_0, x = write_indices_float)[name = tensor("cast_99")]; + tensor write_indices = tile(reps = write_indices_reps_0, x = var_2337)[name = tensor("write_indices")]; + tensor var_2345_begin_0 = const()[name = tensor("op_2345_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2345_end_0 = const()[name = tensor("op_2345_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2345_end_mask_0 = const()[name = tensor("op_2345_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2345_squeeze_mask_0 = const()[name = tensor("op_2345_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2345 = slice_by_index(begin = var_2345_begin_0, end = var_2345_end_0, end_mask = var_2345_end_mask_0, squeeze_mask = var_2345_squeeze_mask_0, x = cache5)[name = tensor("op_2345")]; + tensor var_2347_axis_0 = const()[name = tensor("op_2347_axis_0"), val = tensor(1)]; + tensor var_2347_mode_0 = const()[name = tensor("op_2347_mode_0"), val = tensor("update")]; + tensor var_2347_validate_indices_0 = const()[name = tensor("op_2347_validate_indices_0"), val = tensor(false)]; + tensor var_2347 = scatter_along_axis(axis = var_2347_axis_0, data = var_2345, indices = write_indices, mode = var_2347_mode_0, updates = k, validate_indices = var_2347_validate_indices_0)[name = tensor("op_2347")]; + tensor concat_36 = const()[name = tensor("concat_36"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_20 = const()[name = tensor("shape_20"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_10 = const()[name = tensor("reduce_prod_10"), val = tensor(1048576)]; + tensor range_1d_10_start_0 = const()[name = tensor("range_1d_10_start_0"), val = tensor(0)]; + tensor range_1d_10_step_0 = const()[name = tensor("range_1d_10_step_0"), val = tensor(1)]; + tensor range_1d_10 = range_1d(end = reduce_prod_10, start = range_1d_10_start_0, step = range_1d_10_step_0)[name = tensor("range_1d_10")]; + tensor reshape_50 = reshape(shape = shape_20, x = range_1d_10)[name = tensor("reshape_50")]; + tensor slice_by_index_10 = slice_by_index(begin = concat_36, begin_mask = new_cache_internal_tensor_assign_1_begin_mask_0, end = concat_37, end_mask = new_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_internal_tensor_assign_1_stride_0, x = reshape_50)[name = tensor("slice_by_index_10")]; + tensor reshape_51_shape_0 = const()[name = tensor("reshape_51_shape_0"), val = tensor([-1])]; + tensor reshape_51 = reshape(shape = reshape_51_shape_0, x = slice_by_index_10)[name = tensor("reshape_51")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([-1])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_2347)[name = tensor("reshape_52")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([-1])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = cache5)[name = tensor("reshape_53")]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10_validate_indices_0 = const()[name = tensor("scatter_10_validate_indices_0"), val = tensor(false)]; + tensor scatter_10 = scatter(axis = scatter_10_axis_0, data = reshape_53, indices = reshape_51, mode = scatter_10_mode_0, updates = reshape_52, validate_indices = scatter_10_validate_indices_0)[name = tensor("scatter_10")]; + tensor reshape_54 = reshape(shape = shape_20, x = scatter_10)[name = tensor("reshape_54")]; + tensor var_2355_begin_0 = const()[name = tensor("op_2355_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2355_end_0 = const()[name = tensor("op_2355_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2355_end_mask_0 = const()[name = tensor("op_2355_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2355_squeeze_mask_0 = const()[name = tensor("op_2355_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2355 = slice_by_index(begin = var_2355_begin_0, end = var_2355_end_0, end_mask = var_2355_end_mask_0, squeeze_mask = var_2355_squeeze_mask_0, x = reshape_54)[name = tensor("op_2355")]; + tensor var_2357_axis_0 = const()[name = tensor("op_2357_axis_0"), val = tensor(1)]; + tensor var_2357_mode_0 = const()[name = tensor("op_2357_mode_0"), val = tensor("update")]; + tensor var_2357_validate_indices_0 = const()[name = tensor("op_2357_validate_indices_0"), val = tensor(false)]; + tensor var_2357 = scatter_along_axis(axis = var_2357_axis_0, data = var_2355, indices = write_indices, mode = var_2357_mode_0, updates = v, validate_indices = var_2357_validate_indices_0)[name = tensor("op_2357")]; + tensor concat_38 = const()[name = tensor("concat_38"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_39 = const()[name = tensor("concat_39"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_21 = const()[name = tensor("shape_21"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_11 = const()[name = tensor("reduce_prod_11"), val = tensor(1048576)]; + tensor range_1d_11_start_0 = const()[name = tensor("range_1d_11_start_0"), val = tensor(0)]; + tensor range_1d_11_step_0 = const()[name = tensor("range_1d_11_step_0"), val = tensor(1)]; + tensor range_1d_11 = range_1d(end = reduce_prod_11, start = range_1d_11_start_0, step = range_1d_11_step_0)[name = tensor("range_1d_11")]; + tensor reshape_55 = reshape(shape = shape_21, x = range_1d_11)[name = tensor("reshape_55")]; + tensor slice_by_index_11 = slice_by_index(begin = concat_38, begin_mask = new_cache_internal_tensor_assign_2_begin_mask_0, end = concat_39, end_mask = new_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_internal_tensor_assign_2_stride_0, x = reshape_55)[name = tensor("slice_by_index_11")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([-1])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = slice_by_index_11)[name = tensor("reshape_56")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([-1])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = var_2357)[name = tensor("reshape_57")]; + tensor reshape_58_shape_0 = const()[name = tensor("reshape_58_shape_0"), val = tensor([-1])]; + tensor reshape_58 = reshape(shape = reshape_58_shape_0, x = reshape_54)[name = tensor("reshape_58")]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11_validate_indices_0 = const()[name = tensor("scatter_11_validate_indices_0"), val = tensor(false)]; + tensor scatter_11 = scatter(axis = scatter_11_axis_0, data = reshape_58, indices = reshape_56, mode = scatter_11_mode_0, updates = reshape_57, validate_indices = scatter_11_validate_indices_0)[name = tensor("scatter_11")]; + tensor new_cache_internal_tensor_assign_2 = reshape(shape = shape_21, x = scatter_11)[name = tensor("reshape_59")]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor(0x1p+0)]; + tensor var_2365 = add(x = position5, y = var_2364)[name = tensor("op_2365")]; + } -> (new_cache_1_internal_tensor_assign_2, var_445, new_cache_3_internal_tensor_assign_2, var_864, new_cache_5_internal_tensor_assign_2, var_1283, new_cache_7_internal_tensor_assign_2, var_1702, new_cache_9_internal_tensor_assign_2, var_2121, new_cache_internal_tensor_assign_2, var_2365); +} \ No newline at end of file diff --git a/v2/english/cond_step.mlmodelc/weights/weight.bin b/v2/english/cond_step.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..e6a38ab86f05ec803081140937d18e35ca400ae5 --- /dev/null +++ b/v2/english/cond_step.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f49f0b615e3856b2ec647d3699220893c2be441222694d5c76647d553e69e4b +size 266467520 diff --git a/v2/english/cond_step.mlpackage/Data/com.apple.CoreML/model.mlmodel b/v2/english/cond_step.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..e2e3854aaa2b87aced909060dcd53381cd6469af --- /dev/null +++ b/v2/english/cond_step.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:367c13471a712bba041b013fce8b718dc50389df9c46af5638e49a4b7ca224f2 +size 167298 diff --git a/v2/english/cond_step.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/v2/english/cond_step.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 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"availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-04-24", + "com.github.apple.coremltools.source" : "torch==2.9.1", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1024)", + "shortDescription" : "", + "shape" : "[1, 1024]", + "name" : "transformer_out", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 32)", + "shortDescription" : "", + "shape" : "[1, 32]", + "name" : "latent", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1]", + "name" : "s", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1]", + "name" : "t", + "type" : "MultiArray" + } + ], + "generatedClassName" : "flow_decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/v2/english/flow_decoder.mlmodelc/model.mil b/v2/english/flow_decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..6a099b8e053939b81b2ab0b5aba16b0c7c17a126 --- /dev/null +++ b/v2/english/flow_decoder.mlmodelc/model.mil @@ -0,0 +1,312 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor latent, tensor s, tensor t, tensor transformer_out) { + tensor flow_net_input_proj_bias = const()[name = tensor("flow_net_input_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor flow_net_input_proj_weight = const()[name = tensor("flow_net_input_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2176)))]; + tensor flow_net_time_embed_0_mlp_0_bias = const()[name = tensor("flow_net_time_embed_0_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67776)))]; + tensor flow_net_time_embed_0_mlp_0_weight = const()[name = tensor("flow_net_time_embed_0_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69888)))]; + tensor flow_net_time_embed_0_mlp_2_bias = const()[name = tensor("flow_net_time_embed_0_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(594240)))]; + tensor flow_net_time_embed_0_mlp_2_weight = const()[name = tensor("flow_net_time_embed_0_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596352)))]; + tensor flow_net_time_embed_1_mlp_0_bias = const()[name = tensor("flow_net_time_embed_1_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644992)))]; + tensor flow_net_time_embed_1_mlp_0_weight = const()[name = tensor("flow_net_time_embed_1_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647104)))]; + tensor flow_net_time_embed_1_mlp_2_bias = const()[name = tensor("flow_net_time_embed_1_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2171456)))]; + tensor flow_net_time_embed_1_mlp_2_weight = const()[name = tensor("flow_net_time_embed_1_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173568)))]; + tensor flow_net_cond_embed_bias = const()[name = tensor("flow_net_cond_embed_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3222208)))]; + tensor flow_net_cond_embed_weight = const()[name = tensor("flow_net_cond_embed_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3224320)))]; + tensor flow_net_res_blocks_0_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_0_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5321536)))]; + tensor flow_net_res_blocks_0_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_0_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5327744)))]; + tensor flow_net_res_blocks_0_in_ln_bias = const()[name = tensor("flow_net_res_blocks_0_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8473536)))]; + tensor flow_net_res_blocks_0_in_ln_weight = const()[name = tensor("flow_net_res_blocks_0_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8475648)))]; + tensor flow_net_res_blocks_0_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_0_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8477760)))]; + tensor flow_net_res_blocks_0_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_0_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8479872)))]; + tensor flow_net_res_blocks_0_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_0_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9528512)))]; + tensor flow_net_res_blocks_0_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_0_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9530624)))]; + tensor flow_net_res_blocks_1_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_1_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10579264)))]; + tensor flow_net_res_blocks_1_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_1_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10585472)))]; + tensor flow_net_res_blocks_1_in_ln_bias = const()[name = tensor("flow_net_res_blocks_1_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13731264)))]; + tensor flow_net_res_blocks_1_in_ln_weight = const()[name = tensor("flow_net_res_blocks_1_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13733376)))]; + tensor flow_net_res_blocks_1_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_1_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13735488)))]; + tensor flow_net_res_blocks_1_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_1_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13737600)))]; + tensor flow_net_res_blocks_1_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_1_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14786240)))]; + tensor flow_net_res_blocks_1_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_1_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14788352)))]; + tensor flow_net_res_blocks_2_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_2_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15836992)))]; + tensor flow_net_res_blocks_2_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_2_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15843200)))]; + tensor flow_net_res_blocks_2_in_ln_bias = const()[name = tensor("flow_net_res_blocks_2_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18988992)))]; + tensor flow_net_res_blocks_2_in_ln_weight = const()[name = tensor("flow_net_res_blocks_2_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18991104)))]; + tensor flow_net_res_blocks_2_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_2_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18993216)))]; + tensor flow_net_res_blocks_2_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_2_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18995328)))]; + tensor flow_net_res_blocks_2_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_2_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20043968)))]; + tensor flow_net_res_blocks_2_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_2_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20046080)))]; + tensor flow_net_res_blocks_3_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_3_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21094720)))]; + tensor flow_net_res_blocks_3_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_3_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21100928)))]; + tensor flow_net_res_blocks_3_in_ln_bias = const()[name = tensor("flow_net_res_blocks_3_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24246720)))]; + tensor flow_net_res_blocks_3_in_ln_weight = const()[name = tensor("flow_net_res_blocks_3_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24248832)))]; + tensor flow_net_res_blocks_3_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_3_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24250944)))]; + tensor flow_net_res_blocks_3_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_3_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24253056)))]; + tensor flow_net_res_blocks_3_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_3_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25301696)))]; + tensor flow_net_res_blocks_3_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_3_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25303808)))]; + tensor flow_net_res_blocks_4_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_4_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26352448)))]; + tensor flow_net_res_blocks_4_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_4_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26358656)))]; + tensor flow_net_res_blocks_4_in_ln_bias = const()[name = tensor("flow_net_res_blocks_4_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29504448)))]; + tensor flow_net_res_blocks_4_in_ln_weight = const()[name = tensor("flow_net_res_blocks_4_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29506560)))]; + tensor flow_net_res_blocks_4_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_4_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29508672)))]; + tensor flow_net_res_blocks_4_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_4_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29510784)))]; + tensor flow_net_res_blocks_4_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_4_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30559424)))]; + tensor flow_net_res_blocks_4_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_4_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30561536)))]; + tensor flow_net_res_blocks_5_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_5_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31610176)))]; + tensor flow_net_res_blocks_5_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_5_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31616384)))]; + tensor flow_net_res_blocks_5_in_ln_bias = const()[name = tensor("flow_net_res_blocks_5_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34762176)))]; + tensor flow_net_res_blocks_5_in_ln_weight = const()[name = tensor("flow_net_res_blocks_5_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34764288)))]; + tensor flow_net_res_blocks_5_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_5_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34766400)))]; + tensor flow_net_res_blocks_5_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_5_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34768512)))]; + tensor flow_net_res_blocks_5_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_5_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35817152)))]; + tensor flow_net_res_blocks_5_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_5_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35819264)))]; + tensor flow_net_final_layer_adaLN_modulation_1_bias = const()[name = tensor("flow_net_final_layer_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36867904)))]; + tensor flow_net_final_layer_adaLN_modulation_1_weight = const()[name = tensor("flow_net_final_layer_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36872064)))]; + tensor flow_net_final_layer_linear_bias = const()[name = tensor("flow_net_final_layer_linear_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38969280)))]; + tensor flow_net_final_layer_linear_weight = const()[name = tensor("flow_net_final_layer_linear_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38969472)))]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(-1)]; + tensor x_5 = linear(bias = flow_net_input_proj_bias, weight = flow_net_input_proj_weight, x = latent)[name = tensor("linear_0")]; + tensor const_0 = const()[name = tensor("const_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39035072)))]; + tensor args_1 = mul(x = s, y = const_0)[name = tensor("args_1")]; + tensor var_39 = cos(x = args_1)[name = tensor("op_39")]; + tensor var_40 = sin(x = args_1)[name = tensor("op_40")]; + tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; + tensor input_1 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_39, var_40))[name = tensor("input_1")]; + tensor input_3 = linear(bias = flow_net_time_embed_0_mlp_0_bias, weight = flow_net_time_embed_0_mlp_0_weight, x = input_1)[name = tensor("linear_1")]; + tensor input_5 = silu(x = input_3)[name = tensor("input_5")]; + tensor x_1 = linear(bias = flow_net_time_embed_0_mlp_2_bias, weight = flow_net_time_embed_0_mlp_2_weight, x = input_5)[name = tensor("linear_2")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([-1])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = x_1)[name = tensor("reduce_mean_0")]; + tensor sub_0 = sub(x = x_1, y = reduce_mean_0)[name = tensor("sub_0")]; + tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_1_axes_0 = const()[name = tensor("reduce_mean_1_axes_0"), val = tensor([-1])]; + tensor reduce_mean_1_keep_dims_0 = const()[name = tensor("reduce_mean_1_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_1 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0)[name = tensor("reduce_mean_1")]; + tensor real_div_0 = const()[name = tensor("real_div_0"), val = tensor(0x1.00804p+0)]; + tensor mul_0 = mul(x = reduce_mean_1, y = real_div_0)[name = tensor("mul_0")]; + tensor var_56 = const()[name = tensor("op_56"), val = tensor(0x1.4f8b58p-17)]; + tensor var_1 = add(x = mul_0, y = var_56)[name = tensor("var_1")]; + tensor const_1 = const()[name = tensor("const_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39035648)))]; + tensor var_59_epsilon_0 = const()[name = tensor("op_59_epsilon_0"), val = tensor(0x1.197998p-40)]; + tensor var_59 = rsqrt(epsilon = var_59_epsilon_0, x = var_1)[name = tensor("op_59")]; + tensor var_60 = mul(x = const_1, y = var_59)[name = tensor("op_60")]; + tensor var_61 = mul(x = x_1, y = var_60)[name = tensor("op_61")]; + tensor args = mul(x = t, y = const_0)[name = tensor("args")]; + tensor var_69 = cos(x = args)[name = tensor("op_69")]; + tensor var_70 = sin(x = args)[name = tensor("op_70")]; + tensor input_7_interleave_0 = const()[name = tensor("input_7_interleave_0"), val = tensor(false)]; + tensor input_7 = concat(axis = var_9, interleave = input_7_interleave_0, values = (var_69, var_70))[name = tensor("input_7")]; + tensor input_9 = linear(bias = flow_net_time_embed_1_mlp_0_bias, weight = flow_net_time_embed_1_mlp_0_weight, x = input_7)[name = tensor("linear_3")]; + tensor input_11 = silu(x = input_9)[name = tensor("input_11")]; + tensor x_3 = linear(bias = flow_net_time_embed_1_mlp_2_bias, weight = flow_net_time_embed_1_mlp_2_weight, x = input_11)[name = tensor("linear_4")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([-1])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = x_3)[name = tensor("reduce_mean_2")]; + tensor sub_2 = sub(x = x_3, y = reduce_mean_2)[name = tensor("sub_2")]; + tensor square_1 = square(x = sub_2)[name = tensor("square_1")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([-1])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = square_1)[name = tensor("reduce_mean_3")]; + tensor real_div_1 = const()[name = tensor("real_div_1"), val = tensor(0x1.00804p+0)]; + tensor mul_1 = mul(x = reduce_mean_3, y = real_div_1)[name = tensor("mul_1")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(0x1.4f8b58p-17)]; + tensor var_3 = add(x = mul_1, y = var_86)[name = tensor("var_3")]; + tensor const_3 = const()[name = tensor("const_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39037760)))]; + tensor var_89_epsilon_0 = const()[name = tensor("op_89_epsilon_0"), val = tensor(0x1.197998p-40)]; + tensor var_89 = rsqrt(epsilon = var_89_epsilon_0, x = var_3)[name = tensor("op_89")]; + tensor var_90 = mul(x = const_3, y = var_89)[name = tensor("op_90")]; + tensor var_91 = mul(x = x_3, y = var_90)[name = tensor("op_91")]; + tensor var_93 = add(x = var_61, y = var_91)[name = tensor("op_93")]; + tensor _inversed_t_combined_y_0 = const()[name = tensor("_inversed_t_combined_y_0"), val = tensor(0x1p-1)]; + tensor _inversed_t_combined = mul(x = var_93, y = _inversed_t_combined_y_0)[name = tensor("_inversed_t_combined")]; + tensor c = linear(bias = flow_net_cond_embed_bias, weight = flow_net_cond_embed_weight, x = transformer_out)[name = tensor("linear_5")]; + tensor input_13 = add(x = _inversed_t_combined, y = c)[name = tensor("input_13")]; + tensor input_15 = silu(x = input_13)[name = tensor("input_15")]; + tensor var_107 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_6")]; + tensor var_108_split_sizes_0 = const()[name = tensor("op_108_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_108_axis_0 = const()[name = tensor("op_108_axis_0"), val = tensor(-1)]; + tensor var_108_0, tensor var_108_1, tensor var_108_2 = split(axis = var_108_axis_0, split_sizes = var_108_split_sizes_0, x = var_107)[name = tensor("op_108")]; + tensor mean_1_axes_0 = const()[name = tensor("mean_1_axes_0"), val = tensor([-1])]; + tensor mean_1_keep_dims_0 = const()[name = tensor("mean_1_keep_dims_0"), val = tensor(true)]; + tensor mean_1 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = x_5)[name = tensor("mean_1")]; + tensor sub_4 = sub(x = x_5, y = mean_1)[name = tensor("sub_4")]; + tensor square_2 = square(x = sub_4)[name = tensor("square_2")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([-1])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_2)[name = tensor("reduce_mean_5")]; + tensor var_118 = const()[name = tensor("op_118"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_119 = add(x = reduce_mean_5, y = var_118)[name = tensor("op_119")]; + tensor var_120 = sqrt(x = var_119)[name = tensor("op_120")]; + tensor x_7 = real_div(x = sub_4, y = var_120)[name = tensor("x_7")]; + tensor var_122 = mul(x = x_7, y = flow_net_res_blocks_0_in_ln_weight)[name = tensor("op_122")]; + tensor x_9 = add(x = var_122, y = flow_net_res_blocks_0_in_ln_bias)[name = tensor("x_9")]; + tensor var_124_promoted = const()[name = tensor("op_124_promoted"), val = tensor(0x1p+0)]; + tensor var_125 = add(x = var_108_1, y = var_124_promoted)[name = tensor("op_125")]; + tensor var_126 = mul(x = x_9, y = var_125)[name = tensor("op_126")]; + tensor input_17 = add(x = var_126, y = var_108_0)[name = tensor("input_17")]; + tensor input_19 = linear(bias = flow_net_res_blocks_0_mlp_0_bias, weight = flow_net_res_blocks_0_mlp_0_weight, x = input_17)[name = tensor("linear_7")]; + tensor input_21 = silu(x = input_19)[name = tensor("input_21")]; + tensor h_1 = linear(bias = flow_net_res_blocks_0_mlp_2_bias, weight = flow_net_res_blocks_0_mlp_2_weight, x = input_21)[name = tensor("linear_8")]; + tensor var_137 = mul(x = var_108_2, y = h_1)[name = tensor("op_137")]; + tensor x_11 = add(x = x_5, y = var_137)[name = tensor("x_11")]; + tensor var_146 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_9")]; + tensor var_147_split_sizes_0 = const()[name = tensor("op_147_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_147_axis_0 = const()[name = tensor("op_147_axis_0"), val = tensor(-1)]; + tensor var_147_0, tensor var_147_1, tensor var_147_2 = split(axis = var_147_axis_0, split_sizes = var_147_split_sizes_0, x = var_146)[name = tensor("op_147")]; + tensor mean_3_axes_0 = const()[name = tensor("mean_3_axes_0"), val = tensor([-1])]; + tensor mean_3_keep_dims_0 = const()[name = tensor("mean_3_keep_dims_0"), val = tensor(true)]; + tensor mean_3 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = x_11)[name = tensor("mean_3")]; + tensor sub_5 = sub(x = x_11, y = mean_3)[name = tensor("sub_5")]; + tensor square_3 = square(x = sub_5)[name = tensor("square_3")]; + tensor reduce_mean_7_axes_0 = const()[name = tensor("reduce_mean_7_axes_0"), val = tensor([-1])]; + tensor reduce_mean_7_keep_dims_0 = const()[name = tensor("reduce_mean_7_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_7 = reduce_mean(axes = reduce_mean_7_axes_0, keep_dims = reduce_mean_7_keep_dims_0, x = square_3)[name = tensor("reduce_mean_7")]; + tensor var_157 = const()[name = tensor("op_157"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_158 = add(x = reduce_mean_7, y = var_157)[name = tensor("op_158")]; + tensor var_159 = sqrt(x = var_158)[name = tensor("op_159")]; + tensor x_13 = real_div(x = sub_5, y = var_159)[name = tensor("x_13")]; + tensor var_161 = mul(x = x_13, y = flow_net_res_blocks_1_in_ln_weight)[name = tensor("op_161")]; + tensor x_15 = add(x = var_161, y = flow_net_res_blocks_1_in_ln_bias)[name = tensor("x_15")]; + tensor var_163_promoted = const()[name = tensor("op_163_promoted"), val = tensor(0x1p+0)]; + tensor var_164 = add(x = var_147_1, y = var_163_promoted)[name = tensor("op_164")]; + tensor var_165 = mul(x = x_15, y = var_164)[name = tensor("op_165")]; + tensor input_25 = add(x = var_165, y = var_147_0)[name = tensor("input_25")]; + tensor input_27 = linear(bias = flow_net_res_blocks_1_mlp_0_bias, weight = flow_net_res_blocks_1_mlp_0_weight, x = input_25)[name = tensor("linear_10")]; + tensor input_29 = silu(x = input_27)[name = tensor("input_29")]; + tensor h_3 = linear(bias = flow_net_res_blocks_1_mlp_2_bias, weight = flow_net_res_blocks_1_mlp_2_weight, x = input_29)[name = tensor("linear_11")]; + tensor var_176 = mul(x = var_147_2, y = h_3)[name = tensor("op_176")]; + tensor x_17 = add(x = x_11, y = var_176)[name = tensor("x_17")]; + tensor var_185 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_12")]; + tensor var_186_split_sizes_0 = const()[name = tensor("op_186_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_186_axis_0 = const()[name = tensor("op_186_axis_0"), val = tensor(-1)]; + tensor var_186_0, tensor var_186_1, tensor var_186_2 = split(axis = var_186_axis_0, split_sizes = var_186_split_sizes_0, x = var_185)[name = tensor("op_186")]; + tensor mean_5_axes_0 = const()[name = tensor("mean_5_axes_0"), val = tensor([-1])]; + tensor mean_5_keep_dims_0 = const()[name = tensor("mean_5_keep_dims_0"), val = tensor(true)]; + tensor mean_5 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = x_17)[name = tensor("mean_5")]; + tensor sub_6 = sub(x = x_17, y = mean_5)[name = tensor("sub_6")]; + tensor square_4 = square(x = sub_6)[name = tensor("square_4")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([-1])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = square_4)[name = tensor("reduce_mean_9")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_197 = add(x = reduce_mean_9, y = var_196)[name = tensor("op_197")]; + tensor var_198 = sqrt(x = var_197)[name = tensor("op_198")]; + tensor x_19 = real_div(x = sub_6, y = var_198)[name = tensor("x_19")]; + tensor var_200 = mul(x = x_19, y = flow_net_res_blocks_2_in_ln_weight)[name = tensor("op_200")]; + tensor x_21 = add(x = var_200, y = flow_net_res_blocks_2_in_ln_bias)[name = tensor("x_21")]; + tensor var_202_promoted = const()[name = tensor("op_202_promoted"), val = tensor(0x1p+0)]; + tensor var_203 = add(x = var_186_1, y = var_202_promoted)[name = tensor("op_203")]; + tensor var_204 = mul(x = x_21, y = var_203)[name = tensor("op_204")]; + tensor input_33 = add(x = var_204, y = var_186_0)[name = tensor("input_33")]; + tensor input_35 = linear(bias = flow_net_res_blocks_2_mlp_0_bias, weight = flow_net_res_blocks_2_mlp_0_weight, x = input_33)[name = tensor("linear_13")]; + tensor input_37 = silu(x = input_35)[name = tensor("input_37")]; + tensor h_5 = linear(bias = flow_net_res_blocks_2_mlp_2_bias, weight = flow_net_res_blocks_2_mlp_2_weight, x = input_37)[name = tensor("linear_14")]; + tensor var_215 = mul(x = var_186_2, y = h_5)[name = tensor("op_215")]; + tensor x_23 = add(x = x_17, y = var_215)[name = tensor("x_23")]; + tensor var_224 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_15")]; + tensor var_225_split_sizes_0 = const()[name = tensor("op_225_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_225_axis_0 = const()[name = tensor("op_225_axis_0"), val = tensor(-1)]; + tensor var_225_0, tensor var_225_1, tensor var_225_2 = split(axis = var_225_axis_0, split_sizes = var_225_split_sizes_0, x = var_224)[name = tensor("op_225")]; + tensor mean_7_axes_0 = const()[name = tensor("mean_7_axes_0"), val = tensor([-1])]; + tensor mean_7_keep_dims_0 = const()[name = tensor("mean_7_keep_dims_0"), val = tensor(true)]; + tensor mean_7 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = x_23)[name = tensor("mean_7")]; + tensor sub_7 = sub(x = x_23, y = mean_7)[name = tensor("sub_7")]; + tensor square_5 = square(x = sub_7)[name = tensor("square_5")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([-1])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_5)[name = tensor("reduce_mean_11")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_236 = add(x = reduce_mean_11, y = var_235)[name = tensor("op_236")]; + tensor var_237 = sqrt(x = var_236)[name = tensor("op_237")]; + tensor x_25 = real_div(x = sub_7, y = var_237)[name = tensor("x_25")]; + tensor var_239 = mul(x = x_25, y = flow_net_res_blocks_3_in_ln_weight)[name = tensor("op_239")]; + tensor x_27 = add(x = var_239, y = flow_net_res_blocks_3_in_ln_bias)[name = tensor("x_27")]; + tensor var_241_promoted = const()[name = tensor("op_241_promoted"), val = tensor(0x1p+0)]; + tensor var_242 = add(x = var_225_1, y = var_241_promoted)[name = tensor("op_242")]; + tensor var_243 = mul(x = x_27, y = var_242)[name = tensor("op_243")]; + tensor input_41 = add(x = var_243, y = var_225_0)[name = tensor("input_41")]; + tensor input_43 = linear(bias = flow_net_res_blocks_3_mlp_0_bias, weight = flow_net_res_blocks_3_mlp_0_weight, x = input_41)[name = tensor("linear_16")]; + tensor input_45 = silu(x = input_43)[name = tensor("input_45")]; + tensor h_7 = linear(bias = flow_net_res_blocks_3_mlp_2_bias, weight = flow_net_res_blocks_3_mlp_2_weight, x = input_45)[name = tensor("linear_17")]; + tensor var_254 = mul(x = var_225_2, y = h_7)[name = tensor("op_254")]; + tensor x_29 = add(x = x_23, y = var_254)[name = tensor("x_29")]; + tensor var_263 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_18")]; + tensor var_264_split_sizes_0 = const()[name = tensor("op_264_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_264_axis_0 = const()[name = tensor("op_264_axis_0"), val = tensor(-1)]; + tensor var_264_0, tensor var_264_1, tensor var_264_2 = split(axis = var_264_axis_0, split_sizes = var_264_split_sizes_0, x = var_263)[name = tensor("op_264")]; + tensor mean_9_axes_0 = const()[name = tensor("mean_9_axes_0"), val = tensor([-1])]; + tensor mean_9_keep_dims_0 = const()[name = tensor("mean_9_keep_dims_0"), val = tensor(true)]; + tensor mean_9 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = x_29)[name = tensor("mean_9")]; + tensor sub_8 = sub(x = x_29, y = mean_9)[name = tensor("sub_8")]; + tensor square_6 = square(x = sub_8)[name = tensor("square_6")]; + tensor reduce_mean_13_axes_0 = const()[name = tensor("reduce_mean_13_axes_0"), val = tensor([-1])]; + tensor reduce_mean_13_keep_dims_0 = const()[name = tensor("reduce_mean_13_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_13 = reduce_mean(axes = reduce_mean_13_axes_0, keep_dims = reduce_mean_13_keep_dims_0, x = square_6)[name = tensor("reduce_mean_13")]; + tensor var_274 = const()[name = tensor("op_274"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_275 = add(x = reduce_mean_13, y = var_274)[name = tensor("op_275")]; + tensor var_276 = sqrt(x = var_275)[name = tensor("op_276")]; + tensor x_31 = real_div(x = sub_8, y = var_276)[name = tensor("x_31")]; + tensor var_278 = mul(x = x_31, y = flow_net_res_blocks_4_in_ln_weight)[name = tensor("op_278")]; + tensor x_33 = add(x = var_278, y = flow_net_res_blocks_4_in_ln_bias)[name = tensor("x_33")]; + tensor var_280_promoted = const()[name = tensor("op_280_promoted"), val = tensor(0x1p+0)]; + tensor var_281 = add(x = var_264_1, y = var_280_promoted)[name = tensor("op_281")]; + tensor var_282 = mul(x = x_33, y = var_281)[name = tensor("op_282")]; + tensor input_49 = add(x = var_282, y = var_264_0)[name = tensor("input_49")]; + tensor input_51 = linear(bias = flow_net_res_blocks_4_mlp_0_bias, weight = flow_net_res_blocks_4_mlp_0_weight, x = input_49)[name = tensor("linear_19")]; + tensor input_53 = silu(x = input_51)[name = tensor("input_53")]; + tensor h_9 = linear(bias = flow_net_res_blocks_4_mlp_2_bias, weight = flow_net_res_blocks_4_mlp_2_weight, x = input_53)[name = tensor("linear_20")]; + tensor var_293 = mul(x = var_264_2, y = h_9)[name = tensor("op_293")]; + tensor x_35 = add(x = x_29, y = var_293)[name = tensor("x_35")]; + tensor var_302 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_21")]; + tensor var_303_split_sizes_0 = const()[name = tensor("op_303_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_303_axis_0 = const()[name = tensor("op_303_axis_0"), val = tensor(-1)]; + tensor var_303_0, tensor var_303_1, tensor var_303_2 = split(axis = var_303_axis_0, split_sizes = var_303_split_sizes_0, x = var_302)[name = tensor("op_303")]; + tensor mean_11_axes_0 = const()[name = tensor("mean_11_axes_0"), val = tensor([-1])]; + tensor mean_11_keep_dims_0 = const()[name = tensor("mean_11_keep_dims_0"), val = tensor(true)]; + tensor mean_11 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = x_35)[name = tensor("mean_11")]; + tensor sub_9 = sub(x = x_35, y = mean_11)[name = tensor("sub_9")]; + tensor square_7 = square(x = sub_9)[name = tensor("square_7")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([-1])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = square_7)[name = tensor("reduce_mean_15")]; + tensor var_313 = const()[name = tensor("op_313"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_314 = add(x = reduce_mean_15, y = var_313)[name = tensor("op_314")]; + tensor var_315 = sqrt(x = var_314)[name = tensor("op_315")]; + tensor x_37 = real_div(x = sub_9, y = var_315)[name = tensor("x_37")]; + tensor var_317 = mul(x = x_37, y = flow_net_res_blocks_5_in_ln_weight)[name = tensor("op_317")]; + tensor x_39 = add(x = var_317, y = flow_net_res_blocks_5_in_ln_bias)[name = tensor("x_39")]; + tensor var_319_promoted = const()[name = tensor("op_319_promoted"), val = tensor(0x1p+0)]; + tensor var_320 = add(x = var_303_1, y = var_319_promoted)[name = tensor("op_320")]; + tensor var_321 = mul(x = x_39, y = var_320)[name = tensor("op_321")]; + tensor input_57 = add(x = var_321, y = var_303_0)[name = tensor("input_57")]; + tensor input_59 = linear(bias = flow_net_res_blocks_5_mlp_0_bias, weight = flow_net_res_blocks_5_mlp_0_weight, x = input_57)[name = tensor("linear_22")]; + tensor input_61 = silu(x = input_59)[name = tensor("input_61")]; + tensor h = linear(bias = flow_net_res_blocks_5_mlp_2_bias, weight = flow_net_res_blocks_5_mlp_2_weight, x = input_61)[name = tensor("linear_23")]; + tensor var_332 = mul(x = var_303_2, y = h)[name = tensor("op_332")]; + tensor x_41 = add(x = x_35, y = var_332)[name = tensor("x_41")]; + tensor var_340 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias, weight = flow_net_final_layer_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_24")]; + tensor var_341_split_sizes_0 = const()[name = tensor("op_341_split_sizes_0"), val = tensor([512, 512])]; + tensor var_341_axis_0 = const()[name = tensor("op_341_axis_0"), val = tensor(-1)]; + tensor var_341_0, tensor var_341_1 = split(axis = var_341_axis_0, split_sizes = var_341_split_sizes_0, x = var_340)[name = tensor("op_341")]; + tensor mean_axes_0 = const()[name = tensor("mean_axes_0"), val = tensor([-1])]; + tensor mean_keep_dims_0 = const()[name = tensor("mean_keep_dims_0"), val = tensor(true)]; + tensor mean = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = x_41)[name = tensor("mean")]; + tensor sub_10 = sub(x = x_41, y = mean)[name = tensor("sub_10")]; + tensor square_8 = square(x = sub_10)[name = tensor("square_8")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([-1])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_8)[name = tensor("reduce_mean_17")]; + tensor var_348 = const()[name = tensor("op_348"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_349 = add(x = reduce_mean_17, y = var_348)[name = tensor("op_349")]; + tensor var_350 = sqrt(x = var_349)[name = tensor("op_350")]; + tensor x = real_div(x = sub_10, y = var_350)[name = tensor("x")]; + tensor var_352_promoted = const()[name = tensor("op_352_promoted"), val = tensor(0x1p+0)]; + tensor var_353 = add(x = var_341_1, y = var_352_promoted)[name = tensor("op_353")]; + tensor var_354 = mul(x = x, y = var_353)[name = tensor("op_354")]; + tensor input = add(x = var_354, y = var_341_0)[name = tensor("input")]; + tensor var_358 = linear(bias = flow_net_final_layer_linear_bias, weight = flow_net_final_layer_linear_weight, x = input)[name = tensor("linear_25")]; + } -> (var_358); +} \ No newline at end of file diff --git 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tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor norm0_1_bias = const()[name = tensor("norm0_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131200)))]; + tensor norm0_1_weight = const()[name = tensor("norm0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135360)))]; + tensor attn0_in_proj_weight = const()[name = tensor("attn0_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139520)))]; + tensor attn0_out_proj_weight = const()[name = tensor("attn0_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12722496)))]; + tensor norm0_2_bias = const()[name = tensor("norm0_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16916864)))]; + tensor norm0_2_weight = const()[name = tensor("norm0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16921024)))]; + tensor linear0_1_weight = const()[name = tensor("linear0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16925184)))]; + tensor linear0_2_weight = const()[name = tensor("linear0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33702464)))]; + tensor norm1_1_bias = const()[name = tensor("norm1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50479744)))]; + tensor norm1_1_weight = const()[name = tensor("norm1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50483904)))]; + tensor attn1_in_proj_weight = const()[name = tensor("attn1_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50488064)))]; + tensor attn1_out_proj_weight = const()[name = tensor("attn1_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63071040)))]; + tensor norm1_2_bias = const()[name = tensor("norm1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67265408)))]; + tensor norm1_2_weight = const()[name = tensor("norm1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67269568)))]; + tensor linear1_1_weight = const()[name = tensor("linear1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67273728)))]; + tensor linear1_2_weight = const()[name = tensor("linear1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84051008)))]; + tensor norm2_1_bias = const()[name = tensor("norm2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100828288)))]; + tensor norm2_1_weight = const()[name = tensor("norm2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100832448)))]; + tensor attn2_in_proj_weight = const()[name = tensor("attn2_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100836608)))]; + tensor attn2_out_proj_weight = const()[name = tensor("attn2_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113419584)))]; + tensor norm2_2_bias = const()[name = tensor("norm2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117613952)))]; + tensor norm2_2_weight = const()[name = tensor("norm2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117618112)))]; + tensor linear2_1_weight = const()[name = tensor("linear2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117622272)))]; + tensor linear2_2_weight = const()[name = tensor("linear2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134399552)))]; + tensor norm3_1_bias = const()[name = tensor("norm3_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151176832)))]; + tensor norm3_1_weight = const()[name = tensor("norm3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151180992)))]; + tensor attn3_in_proj_weight = const()[name = tensor("attn3_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151185152)))]; + tensor attn3_out_proj_weight = const()[name = tensor("attn3_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163768128)))]; + tensor norm3_2_bias = const()[name = tensor("norm3_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167962496)))]; + tensor norm3_2_weight = const()[name = tensor("norm3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167966656)))]; + tensor linear3_1_weight = const()[name = tensor("linear3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167970816)))]; + tensor linear3_2_weight = const()[name = tensor("linear3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184748096)))]; + tensor norm4_1_bias = const()[name = tensor("norm4_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201525376)))]; + tensor norm4_1_weight = const()[name = tensor("norm4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201529536)))]; + tensor attn4_in_proj_weight = const()[name = tensor("attn4_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201533696)))]; + tensor attn4_out_proj_weight = const()[name = tensor("attn4_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214116672)))]; + tensor norm4_2_bias = const()[name = tensor("norm4_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218311040)))]; + tensor norm4_2_weight = const()[name = tensor("norm4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218315200)))]; + tensor linear4_1_weight = const()[name = tensor("linear4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218319360)))]; + tensor linear4_2_weight = const()[name = tensor("linear4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235096640)))]; + tensor norm5_1_bias = const()[name = tensor("norm5_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251873920)))]; + tensor norm5_1_weight = const()[name = tensor("norm5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251878080)))]; + tensor attn5_in_proj_weight = const()[name = tensor("attn5_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251882240)))]; + tensor attn5_out_proj_weight = const()[name = tensor("attn5_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264465216)))]; + tensor norm5_2_bias = const()[name = tensor("norm5_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268659584)))]; + tensor norm5_2_weight = const()[name = tensor("norm5_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268663744)))]; + tensor linear5_1_weight = const()[name = tensor("linear5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268667904)))]; + tensor linear5_2_weight = const()[name = tensor("linear5_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285445184)))]; + tensor out_norm_bias = const()[name = tensor("out_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302222464)))]; + tensor out_norm_weight = const()[name = tensor("out_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302226624)))]; + tensor out_eos_bias = const()[name = tensor("out_eos_bias"), val = tensor([-0x1.36p-2])]; + tensor out_eos_weight = const()[name = tensor("out_eos_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302230784)))]; + tensor var_54 = not_equal(x = sequence, y = sequence)[name = tensor("op_54")]; + tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0, 1])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = bos_emb)[name = tensor("expand_dims_0")]; + tensor input_1 = select(a = expand_dims_0, b = sequence, cond = var_54)[name = tensor("input_1")]; + tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302234944)))]; + tensor input_3 = linear(bias = linear_0_bias_0, weight = input_linear_weight, x = input_1)[name = tensor("linear_0")]; + tensor var_60 = const()[name = tensor("op_60"), val = tensor(0x1.4f8b58p-17)]; + tensor x_1_axes_0 = const()[name = tensor("x_1_axes_0"), val = tensor([-1])]; + tensor x_1 = layer_norm(axes = x_1_axes_0, beta = norm0_1_bias, epsilon = var_60, gamma = norm0_1_weight, x = input_3)[name = tensor("x_1")]; + tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302239104)))]; + tensor var_92 = linear(bias = linear_1_bias_0, weight = attn0_in_proj_weight, x = x_1)[name = tensor("linear_1")]; + tensor var_96 = const()[name = tensor("op_96"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_1 = reshape(shape = var_96, x = var_92)[name = tensor("qkv_1")]; + tensor q_1_begin_0 = const()[name = tensor("q_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_1_end_0 = const()[name = tensor("q_1_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_1_end_mask_0 = const()[name = tensor("q_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_1_squeeze_mask_0 = const()[name = tensor("q_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_1 = slice_by_index(begin = q_1_begin_0, end = q_1_end_0, end_mask = q_1_end_mask_0, squeeze_mask = q_1_squeeze_mask_0, x = qkv_1)[name = tensor("q_1")]; + tensor k_1_begin_0 = const()[name = tensor("k_1_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_1_end_0 = const()[name = tensor("k_1_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_1_end_mask_0 = const()[name = tensor("k_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_1_squeeze_mask_0 = const()[name = tensor("k_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_1 = slice_by_index(begin = k_1_begin_0, end = k_1_end_0, end_mask = k_1_end_mask_0, squeeze_mask = k_1_squeeze_mask_0, x = qkv_1)[name = tensor("k_1")]; + tensor v_1_begin_0 = const()[name = tensor("v_1_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_1_end_0 = const()[name = tensor("v_1_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_1_end_mask_0 = const()[name = tensor("v_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_1_squeeze_mask_0 = const()[name = tensor("v_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_1 = slice_by_index(begin = v_1_begin_0, end = v_1_end_0, end_mask = v_1_end_mask_0, squeeze_mask = v_1_squeeze_mask_0, x = qkv_1)[name = tensor("v_1")]; + tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302251456)))]; + tensor var_200 = const()[name = tensor("op_200"), val = tensor([1, 1, 1, 1])]; + tensor ts_5 = reshape(shape = var_200, x = position0)[name = tensor("ts_5")]; + tensor var_204 = const()[name = tensor("op_204"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_1 = reshape(shape = var_204, x = q_1)[name = tensor("q_complex_1")]; + tensor var_208 = const()[name = tensor("op_208"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_1 = reshape(shape = var_208, x = k_1)[name = tensor("k_complex_1")]; + tensor var_212_begin_0 = const()[name = tensor("op_212_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_212_end_0 = const()[name = tensor("op_212_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_212_end_mask_0 = const()[name = tensor("op_212_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_212_squeeze_mask_0 = const()[name = tensor("op_212_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_212 = slice_by_index(begin = var_212_begin_0, end = var_212_end_0, end_mask = var_212_end_mask_0, squeeze_mask = var_212_squeeze_mask_0, x = q_complex_1)[name = tensor("op_212")]; + tensor var_220_begin_0 = const()[name = tensor("op_220_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_220_end_0 = const()[name = tensor("op_220_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_220_end_mask_0 = const()[name = tensor("op_220_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_220_squeeze_mask_0 = const()[name = tensor("op_220_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_220 = slice_by_index(begin = var_220_begin_0, end = var_220_end_0, end_mask = var_220_end_mask_0, squeeze_mask = var_220_squeeze_mask_0, x = q_complex_1)[name = tensor("op_220")]; + tensor var_228_begin_0 = const()[name = tensor("op_228_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_228_end_0 = const()[name = tensor("op_228_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_228_end_mask_0 = const()[name = tensor("op_228_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_228_squeeze_mask_0 = const()[name = tensor("op_228_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_228 = slice_by_index(begin = var_228_begin_0, end = var_228_end_0, end_mask = var_228_end_mask_0, squeeze_mask = var_228_squeeze_mask_0, x = k_complex_1)[name = tensor("op_228")]; + tensor var_236_begin_0 = const()[name = tensor("op_236_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_236_end_0 = const()[name = tensor("op_236_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_236_end_mask_0 = const()[name = tensor("op_236_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_236_squeeze_mask_0 = const()[name = tensor("op_236_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_236 = slice_by_index(begin = var_236_begin_0, end = var_236_end_0, end_mask = var_236_end_mask_0, squeeze_mask = var_236_squeeze_mask_0, x = k_complex_1)[name = tensor("op_236")]; + tensor var_242 = mul(x = freqs_1, y = ts_5)[name = tensor("op_242")]; + tensor rotr_1 = cos(x = var_242)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_242)[name = tensor("roti_1")]; + tensor var_246 = mul(x = var_212, y = rotr_1)[name = tensor("op_246")]; + tensor var_247 = mul(x = var_220, y = roti_1)[name = tensor("op_247")]; + tensor qor_1 = sub(x = var_246, y = var_247)[name = tensor("qor_1")]; + tensor var_250 = mul(x = var_212, y = roti_1)[name = tensor("op_250")]; + tensor var_251 = mul(x = var_220, y = rotr_1)[name = tensor("op_251")]; + tensor qoi_1 = add(x = var_250, y = var_251)[name = tensor("qoi_1")]; + tensor var_254 = mul(x = var_228, y = rotr_1)[name = tensor("op_254")]; + tensor var_255 = mul(x = var_236, y = roti_1)[name = tensor("op_255")]; + tensor kor_1 = sub(x = var_254, y = var_255)[name = tensor("kor_1")]; + tensor var_258 = mul(x = var_228, y = roti_1)[name = tensor("op_258")]; + tensor var_259 = mul(x = var_236, y = rotr_1)[name = tensor("op_259")]; + tensor koi_1 = add(x = var_258, y = var_259)[name = tensor("koi_1")]; + tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; + tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; + tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; + tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; + tensor var_288 = const()[name = tensor("op_288"), val = tensor([1, 1, 16, 64])]; + tensor q_3 = reshape(shape = var_288, x = qo_1)[name = tensor("q_3")]; + tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1, 16, 64])]; + tensor k_3 = reshape(shape = var_290, x = ko_1)[name = tensor("k_3")]; + tensor _inversed_312_y_0 = const()[name = tensor("_inversed_312_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_312 = mul(x = ts_5, y = _inversed_312_y_0)[name = tensor("_inversed_312")]; + tensor var_313 = floor(x = _inversed_312)[name = tensor("op_313")]; + tensor var_314 = const()[name = tensor("op_314"), val = tensor(0x1p+9)]; + tensor var_315 = mul(x = var_313, y = var_314)[name = tensor("op_315")]; + tensor write_indices_float_3 = sub(x = ts_5, y = var_315)[name = tensor("write_indices_float_3")]; + tensor var_322_dtype_0 = const()[name = tensor("op_322_dtype_0"), val = tensor("int32")]; + tensor write_indices_1_reps_0 = const()[name = tensor("write_indices_1_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_322 = cast(dtype = var_322_dtype_0, x = write_indices_float_3)[name = tensor("cast_113")]; + tensor write_indices_1 = tile(reps = write_indices_1_reps_0, x = var_322)[name = tensor("write_indices_1")]; + tensor var_330_begin_0 = const()[name = tensor("op_330_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_330_end_0 = const()[name = tensor("op_330_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_330_end_mask_0 = const()[name = tensor("op_330_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_330_squeeze_mask_0 = const()[name = tensor("op_330_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_330 = slice_by_index(begin = var_330_begin_0, end = var_330_end_0, end_mask = var_330_end_mask_0, squeeze_mask = var_330_squeeze_mask_0, x = cache0)[name = tensor("op_330")]; + tensor var_332_axis_0 = const()[name = tensor("op_332_axis_0"), val = tensor(1)]; + tensor var_332_mode_0 = const()[name = tensor("op_332_mode_0"), val = tensor("update")]; + tensor var_332_validate_indices_0 = const()[name = tensor("op_332_validate_indices_0"), val = tensor(false)]; + tensor var_332 = scatter_along_axis(axis = var_332_axis_0, data = var_330, indices = write_indices_1, mode = var_332_mode_0, updates = k_3, validate_indices = var_332_validate_indices_0)[name = tensor("op_332")]; + tensor concat_2 = const()[name = tensor("concat_2"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_3 = const()[name = tensor("concat_3"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_12 = const()[name = tensor("shape_12"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_0 = const()[name = tensor("reduce_prod_0"), val = tensor(1048576)]; + tensor range_1d_0_start_0 = const()[name = tensor("range_1d_0_start_0"), val = tensor(0)]; + tensor range_1d_0_step_0 = const()[name = tensor("range_1d_0_step_0"), val = tensor(1)]; + tensor range_1d_0 = range_1d(end = reduce_prod_0, start = range_1d_0_start_0, step = range_1d_0_step_0)[name = tensor("range_1d_0")]; + tensor reshape_0 = reshape(shape = shape_12, x = range_1d_0)[name = tensor("reshape_0")]; + tensor slice_by_index_0 = slice_by_index(begin = concat_2, begin_mask = new_cache_1_internal_tensor_assign_1_begin_mask_0, end = concat_3, end_mask = new_cache_1_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_1_stride_0, x = reshape_0)[name = tensor("slice_by_index_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([-1])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = slice_by_index_0)[name = tensor("reshape_1")]; + tensor reshape_2_shape_0 = const()[name = tensor("reshape_2_shape_0"), val = tensor([-1])]; + tensor reshape_2 = reshape(shape = reshape_2_shape_0, x = var_332)[name = tensor("reshape_2")]; + tensor reshape_3_shape_0 = const()[name = tensor("reshape_3_shape_0"), val = tensor([-1])]; + tensor reshape_3 = reshape(shape = reshape_3_shape_0, x = cache0)[name = tensor("reshape_3")]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor scatter_0_validate_indices_0 = const()[name = tensor("scatter_0_validate_indices_0"), val = tensor(false)]; + tensor scatter_0 = scatter(axis = scatter_0_axis_0, data = reshape_3, indices = reshape_1, mode = scatter_0_mode_0, updates = reshape_2, validate_indices = scatter_0_validate_indices_0)[name = tensor("scatter_0")]; + tensor reshape_4 = reshape(shape = shape_12, x = scatter_0)[name = tensor("reshape_4")]; + tensor var_340_begin_0 = const()[name = tensor("op_340_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_340_end_0 = const()[name = tensor("op_340_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_340_end_mask_0 = const()[name = tensor("op_340_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_340_squeeze_mask_0 = const()[name = tensor("op_340_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_340 = slice_by_index(begin = var_340_begin_0, end = var_340_end_0, end_mask = var_340_end_mask_0, squeeze_mask = var_340_squeeze_mask_0, x = reshape_4)[name = tensor("op_340")]; + tensor var_342_axis_0 = const()[name = tensor("op_342_axis_0"), val = tensor(1)]; + tensor var_342_mode_0 = const()[name = tensor("op_342_mode_0"), val = tensor("update")]; + tensor var_342_validate_indices_0 = const()[name = tensor("op_342_validate_indices_0"), val = tensor(false)]; + tensor var_342 = scatter_along_axis(axis = var_342_axis_0, data = var_340, indices = write_indices_1, mode = var_342_mode_0, updates = v_1, validate_indices = var_342_validate_indices_0)[name = tensor("op_342")]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_5 = const()[name = tensor("concat_5"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_13 = const()[name = tensor("shape_13"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_1 = const()[name = tensor("reduce_prod_1"), val = tensor(1048576)]; + tensor range_1d_1_start_0 = const()[name = tensor("range_1d_1_start_0"), val = tensor(0)]; + tensor range_1d_1_step_0 = const()[name = tensor("range_1d_1_step_0"), val = tensor(1)]; + tensor range_1d_1 = range_1d(end = reduce_prod_1, start = range_1d_1_start_0, step = range_1d_1_step_0)[name = tensor("range_1d_1")]; + tensor reshape_5 = reshape(shape = shape_13, x = range_1d_1)[name = tensor("reshape_5")]; + tensor slice_by_index_1 = slice_by_index(begin = concat_4, begin_mask = new_cache_1_internal_tensor_assign_2_begin_mask_0, end = concat_5, end_mask = new_cache_1_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_2_stride_0, x = reshape_5)[name = tensor("slice_by_index_1")]; + tensor reshape_6_shape_0 = const()[name = tensor("reshape_6_shape_0"), val = tensor([-1])]; + tensor reshape_6 = reshape(shape = reshape_6_shape_0, x = slice_by_index_1)[name = tensor("reshape_6")]; + tensor reshape_7_shape_0 = const()[name = tensor("reshape_7_shape_0"), val = tensor([-1])]; + tensor reshape_7 = reshape(shape = reshape_7_shape_0, x = var_342)[name = tensor("reshape_7")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([-1])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = reshape_4)[name = tensor("reshape_8")]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor scatter_1_validate_indices_0 = const()[name = tensor("scatter_1_validate_indices_0"), val = tensor(false)]; + tensor scatter_1 = scatter(axis = scatter_1_axis_0, data = reshape_8, indices = reshape_6, mode = scatter_1_mode_0, updates = reshape_7, validate_indices = scatter_1_validate_indices_0)[name = tensor("scatter_1")]; + tensor new_cache_1_internal_tensor_assign_2 = reshape(shape = shape_13, x = scatter_1)[name = tensor("reshape_9")]; + tensor keys_1_begin_0 = const()[name = tensor("keys_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_1_end_0 = const()[name = tensor("keys_1_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_1_end_mask_0 = const()[name = tensor("keys_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_1_squeeze_mask_0 = const()[name = tensor("keys_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_1 = slice_by_index(begin = keys_1_begin_0, end = keys_1_end_0, end_mask = keys_1_end_mask_0, squeeze_mask = keys_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("keys_1")]; + tensor values_1_begin_0 = const()[name = tensor("values_1_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_1_end_0 = const()[name = tensor("values_1_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_1_end_mask_0 = const()[name = tensor("values_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_1_squeeze_mask_0 = const()[name = tensor("values_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_1 = slice_by_index(begin = values_1_begin_0, end = values_1_end_0, end_mask = values_1_end_mask_0, squeeze_mask = values_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("values_1")]; + tensor var_354 = not_equal(x = keys_1, y = keys_1)[name = tensor("op_354")]; + tensor var_360 = const()[name = tensor("op_360"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302251648)))]; + tensor keys_3 = select(a = var_360, b = keys_1, cond = var_354)[name = tensor("keys_3")]; + tensor var_362 = not_equal(x = values_1, y = values_1)[name = tensor("op_362")]; + tensor values_3 = select(a = var_360, b = values_1, cond = var_362)[name = tensor("values_3")]; + tensor var_386 = const()[name = tensor("op_386"), val = tensor([0, 2, 1, 3])]; + tensor var_399 = const()[name = tensor("op_399"), val = tensor([1, 1, 1])]; + tensor var_400 = reshape(shape = var_399, x = position0)[name = tensor("op_400")]; + tensor var_417 = const()[name = tensor("op_417"), val = tensor(0x1p+0)]; + tensor valid_len_1 = add(x = var_400, y = var_417)[name = tensor("valid_len_1")]; + tensor k_positions_1_promoted = const()[name = tensor("k_positions_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304348864)))]; + tensor valid_mask_1 = less(x = k_positions_1_promoted, y = valid_len_1)[name = tensor("valid_mask_1")]; + tensor causal_mask_1 = less_equal(x = k_positions_1_promoted, y = var_400)[name = tensor("causal_mask_1")]; + tensor attn_mask_1 = logical_and(x = valid_mask_1, y = causal_mask_1)[name = tensor("attn_mask_1")]; + tensor attn_mask_3_axes_0 = const()[name = tensor("attn_mask_3_axes_0"), val = tensor([1])]; + tensor attn_mask_3 = expand_dims(axes = attn_mask_3_axes_0, x = attn_mask_1)[name = tensor("attn_mask_3")]; + tensor var_429 = const()[name = tensor("op_429"), val = tensor([0x1.fffe5cp-4])]; + tensor var_435_transpose_x_0 = const()[name = tensor("op_435_transpose_x_0"), val = tensor(false)]; + tensor var_435_transpose_y_0 = const()[name = tensor("op_435_transpose_y_0"), val = tensor(false)]; + tensor transpose_18_perm_0 = const()[name = tensor("transpose_18_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_19_perm_0 = const()[name = tensor("transpose_19_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_19 = transpose(perm = transpose_19_perm_0, x = keys_3)[name = tensor("transpose_51")]; + tensor transpose_18 = transpose(perm = transpose_18_perm_0, x = q_3)[name = tensor("transpose_52")]; + tensor var_435 = matmul(transpose_x = var_435_transpose_x_0, transpose_y = var_435_transpose_y_0, x = transpose_18, y = transpose_19)[name = tensor("op_435")]; + tensor attn_weights_1 = mul(x = var_435, y = var_429)[name = tensor("attn_weights_1")]; + tensor var_437 = logical_not(x = attn_mask_3)[name = tensor("op_437")]; + tensor var_438 = const()[name = tensor("op_438"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_3 = select(a = var_438, b = attn_weights_1, cond = var_437)[name = tensor("attn_weights_3")]; + tensor var_440 = const()[name = tensor("op_440"), val = tensor(-1)]; + tensor attn_weights_5 = softmax(axis = var_440, x = attn_weights_3)[name = tensor("attn_weights_5")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor values_5 = transpose(perm = var_386, x = values_3)[name = tensor("transpose_53")]; + tensor attn_output_1 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = attn_weights_5, y = values_5)[name = tensor("attn_output_1")]; + tensor var_448 = const()[name = tensor("op_448"), val = tensor([0, 2, 1, 3])]; + tensor var_451 = const()[name = tensor("op_451"), val = tensor([1, 1, 1024])]; + tensor var_449 = transpose(perm = var_448, x = attn_output_1)[name = tensor("transpose_50")]; + tensor input_5 = reshape(shape = var_451, x = var_449)[name = tensor("input_5")]; + tensor attn_out_1 = linear(bias = linear_0_bias_0, weight = attn0_out_proj_weight, x = input_5)[name = tensor("linear_2")]; + tensor var_457 = const()[name = tensor("op_457"), val = tensor(0x1p+0)]; + tensor var_458 = add(x = position0, y = var_457)[name = tensor("op_458")]; + tensor input_7 = add(x = input_3, y = attn_out_1)[name = tensor("input_7")]; + tensor var_462 = const()[name = tensor("op_462"), val = tensor(0x1.4f8b58p-17)]; + tensor input_9_axes_0 = const()[name = tensor("input_9_axes_0"), val = tensor([-1])]; + tensor input_9 = layer_norm(axes = input_9_axes_0, beta = norm0_2_bias, epsilon = var_462, gamma = norm0_2_weight, x = input_7)[name = tensor("input_9")]; + tensor linear_3_bias_0 = const()[name = tensor("linear_3_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304350976)))]; + tensor var_470 = linear(bias = linear_3_bias_0, weight = linear0_1_weight, x = input_9)[name = tensor("linear_3")]; + tensor input_11_mode_0 = const()[name = tensor("input_11_mode_0"), val = tensor("EXACT")]; + tensor input_11 = gelu(mode = input_11_mode_0, x = var_470)[name = tensor("input_11")]; + tensor ffn_out_1 = linear(bias = linear_0_bias_0, weight = linear0_2_weight, x = input_11)[name = tensor("linear_4")]; + tensor input_13 = add(x = input_7, y = ffn_out_1)[name = tensor("input_13")]; + tensor var_479 = const()[name = tensor("op_479"), val = tensor(0x1.4f8b58p-17)]; + tensor x_3_axes_0 = const()[name = tensor("x_3_axes_0"), val = tensor([-1])]; + tensor x_3 = layer_norm(axes = x_3_axes_0, beta = norm1_1_bias, epsilon = var_479, gamma = norm1_1_weight, x = input_13)[name = tensor("x_3")]; + tensor var_511 = linear(bias = linear_1_bias_0, weight = attn1_in_proj_weight, x = x_3)[name = tensor("linear_5")]; + tensor var_515 = const()[name = tensor("op_515"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_3 = reshape(shape = var_515, x = var_511)[name = tensor("qkv_3")]; + tensor q_7_begin_0 = const()[name = tensor("q_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_7_end_0 = const()[name = tensor("q_7_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_7_end_mask_0 = const()[name = tensor("q_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_7_squeeze_mask_0 = const()[name = tensor("q_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_7 = slice_by_index(begin = q_7_begin_0, end = q_7_end_0, end_mask = q_7_end_mask_0, squeeze_mask = q_7_squeeze_mask_0, x = qkv_3)[name = tensor("q_7")]; + tensor k_5_begin_0 = const()[name = tensor("k_5_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_5_end_0 = const()[name = tensor("k_5_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_5_end_mask_0 = const()[name = tensor("k_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_5_squeeze_mask_0 = const()[name = tensor("k_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_5 = slice_by_index(begin = k_5_begin_0, end = k_5_end_0, end_mask = k_5_end_mask_0, squeeze_mask = k_5_squeeze_mask_0, x = qkv_3)[name = tensor("k_5")]; + tensor v_3_begin_0 = const()[name = tensor("v_3_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_3_end_0 = const()[name = tensor("v_3_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_3_end_mask_0 = const()[name = tensor("v_3_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_3_squeeze_mask_0 = const()[name = tensor("v_3_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_3 = slice_by_index(begin = v_3_begin_0, end = v_3_end_0, end_mask = v_3_end_mask_0, squeeze_mask = v_3_squeeze_mask_0, x = qkv_3)[name = tensor("v_3")]; + tensor freqs_3 = const()[name = tensor("freqs_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304367424)))]; + tensor var_619 = const()[name = tensor("op_619"), val = tensor([1, 1, 1, 1])]; + tensor ts_11 = reshape(shape = var_619, x = position1)[name = tensor("ts_11")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_3 = reshape(shape = var_623, x = q_7)[name = tensor("q_complex_3")]; + tensor var_627 = const()[name = tensor("op_627"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_3 = reshape(shape = var_627, x = k_5)[name = tensor("k_complex_3")]; + tensor var_631_begin_0 = const()[name = tensor("op_631_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_631_end_0 = const()[name = tensor("op_631_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_631_end_mask_0 = const()[name = tensor("op_631_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_631_squeeze_mask_0 = const()[name = tensor("op_631_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_631 = slice_by_index(begin = var_631_begin_0, end = var_631_end_0, end_mask = var_631_end_mask_0, squeeze_mask = var_631_squeeze_mask_0, x = q_complex_3)[name = tensor("op_631")]; + tensor var_639_begin_0 = const()[name = tensor("op_639_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_639_end_0 = const()[name = tensor("op_639_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_639_end_mask_0 = const()[name = tensor("op_639_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_639_squeeze_mask_0 = const()[name = tensor("op_639_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_639 = slice_by_index(begin = var_639_begin_0, end = var_639_end_0, end_mask = var_639_end_mask_0, squeeze_mask = var_639_squeeze_mask_0, x = q_complex_3)[name = tensor("op_639")]; + tensor var_647_begin_0 = const()[name = tensor("op_647_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_647_end_0 = const()[name = tensor("op_647_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_647_end_mask_0 = const()[name = tensor("op_647_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_647_squeeze_mask_0 = const()[name = tensor("op_647_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_647 = slice_by_index(begin = var_647_begin_0, end = var_647_end_0, end_mask = var_647_end_mask_0, squeeze_mask = var_647_squeeze_mask_0, x = k_complex_3)[name = tensor("op_647")]; + tensor var_655_begin_0 = const()[name = tensor("op_655_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_655_end_0 = const()[name = tensor("op_655_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_655_end_mask_0 = const()[name = tensor("op_655_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_655_squeeze_mask_0 = const()[name = tensor("op_655_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_655 = slice_by_index(begin = var_655_begin_0, end = var_655_end_0, end_mask = var_655_end_mask_0, squeeze_mask = var_655_squeeze_mask_0, x = k_complex_3)[name = tensor("op_655")]; + tensor var_661 = mul(x = freqs_3, y = ts_11)[name = tensor("op_661")]; + tensor rotr_3 = cos(x = var_661)[name = tensor("rotr_3")]; + tensor roti_3 = sin(x = var_661)[name = tensor("roti_3")]; + tensor var_665 = mul(x = var_631, y = rotr_3)[name = tensor("op_665")]; + tensor var_666 = mul(x = var_639, y = roti_3)[name = tensor("op_666")]; + tensor qor_5 = sub(x = var_665, y = var_666)[name = tensor("qor_5")]; + tensor var_669 = mul(x = var_631, y = roti_3)[name = tensor("op_669")]; + tensor var_670 = mul(x = var_639, y = rotr_3)[name = tensor("op_670")]; + tensor qoi_5 = add(x = var_669, y = var_670)[name = tensor("qoi_5")]; + tensor var_673 = mul(x = var_647, y = rotr_3)[name = tensor("op_673")]; + tensor var_674 = mul(x = var_655, y = roti_3)[name = tensor("op_674")]; + tensor kor_5 = sub(x = var_673, y = var_674)[name = tensor("kor_5")]; + tensor var_677 = mul(x = var_647, y = roti_3)[name = tensor("op_677")]; + tensor var_678 = mul(x = var_655, y = rotr_3)[name = tensor("op_678")]; + tensor koi_5 = add(x = var_677, y = var_678)[name = tensor("koi_5")]; + tensor qo_3_axis_0 = const()[name = tensor("qo_3_axis_0"), val = tensor(-1)]; + tensor qo_3 = stack(axis = qo_3_axis_0, values = (qor_5, qoi_5))[name = tensor("qo_3")]; + tensor ko_3_axis_0 = const()[name = tensor("ko_3_axis_0"), val = tensor(-1)]; + tensor ko_3 = stack(axis = ko_3_axis_0, values = (kor_5, koi_5))[name = tensor("ko_3")]; + tensor var_707 = const()[name = tensor("op_707"), val = tensor([1, 1, 16, 64])]; + tensor q_9 = reshape(shape = var_707, x = qo_3)[name = tensor("q_9")]; + tensor var_709 = const()[name = tensor("op_709"), val = tensor([1, 1, 16, 64])]; + tensor k_7 = reshape(shape = var_709, x = ko_3)[name = tensor("k_7")]; + tensor _inversed_731_y_0 = const()[name = tensor("_inversed_731_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_731 = mul(x = ts_11, y = _inversed_731_y_0)[name = tensor("_inversed_731")]; + tensor var_732 = floor(x = _inversed_731)[name = tensor("op_732")]; + tensor var_733 = const()[name = tensor("op_733"), val = tensor(0x1p+9)]; + tensor var_734 = mul(x = var_732, y = var_733)[name = tensor("op_734")]; + tensor write_indices_float_7 = sub(x = ts_11, y = var_734)[name = tensor("write_indices_float_7")]; + tensor var_741_dtype_0 = const()[name = tensor("op_741_dtype_0"), val = tensor("int32")]; + tensor write_indices_3_reps_0 = const()[name = tensor("write_indices_3_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_741 = cast(dtype = var_741_dtype_0, x = write_indices_float_7)[name = tensor("cast_112")]; + tensor write_indices_3 = tile(reps = write_indices_3_reps_0, x = var_741)[name = tensor("write_indices_3")]; + tensor var_749_begin_0 = const()[name = tensor("op_749_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_749_end_0 = const()[name = tensor("op_749_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_749_end_mask_0 = const()[name = tensor("op_749_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_749_squeeze_mask_0 = const()[name = tensor("op_749_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_749 = slice_by_index(begin = var_749_begin_0, end = var_749_end_0, end_mask = var_749_end_mask_0, squeeze_mask = var_749_squeeze_mask_0, x = cache1)[name = tensor("op_749")]; + tensor var_751_axis_0 = const()[name = tensor("op_751_axis_0"), val = tensor(1)]; + tensor var_751_mode_0 = const()[name = tensor("op_751_mode_0"), val = tensor("update")]; + tensor var_751_validate_indices_0 = const()[name = tensor("op_751_validate_indices_0"), val = tensor(false)]; + tensor var_751 = scatter_along_axis(axis = var_751_axis_0, data = var_749, indices = write_indices_3, mode = var_751_mode_0, updates = k_7, validate_indices = var_751_validate_indices_0)[name = tensor("op_751")]; + tensor concat_9 = const()[name = tensor("concat_9"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_10 = const()[name = tensor("concat_10"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_14 = const()[name = tensor("shape_14"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_2 = const()[name = tensor("reduce_prod_2"), val = tensor(1048576)]; + tensor range_1d_2_start_0 = const()[name = tensor("range_1d_2_start_0"), val = tensor(0)]; + tensor range_1d_2_step_0 = const()[name = tensor("range_1d_2_step_0"), val = tensor(1)]; + tensor range_1d_2 = range_1d(end = reduce_prod_2, start = range_1d_2_start_0, step = range_1d_2_step_0)[name = tensor("range_1d_2")]; + tensor reshape_10 = reshape(shape = shape_14, x = range_1d_2)[name = tensor("reshape_10")]; + tensor slice_by_index_2 = slice_by_index(begin = concat_9, begin_mask = new_cache_3_internal_tensor_assign_1_begin_mask_0, end = concat_10, end_mask = new_cache_3_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_1_stride_0, x = reshape_10)[name = tensor("slice_by_index_2")]; + tensor reshape_11_shape_0 = const()[name = tensor("reshape_11_shape_0"), val = tensor([-1])]; + tensor reshape_11 = reshape(shape = reshape_11_shape_0, x = slice_by_index_2)[name = tensor("reshape_11")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([-1])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = var_751)[name = tensor("reshape_12")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([-1])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = cache1)[name = tensor("reshape_13")]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2_validate_indices_0 = const()[name = tensor("scatter_2_validate_indices_0"), val = tensor(false)]; + tensor scatter_2 = scatter(axis = scatter_2_axis_0, data = reshape_13, indices = reshape_11, mode = scatter_2_mode_0, updates = reshape_12, validate_indices = scatter_2_validate_indices_0)[name = tensor("scatter_2")]; + tensor reshape_14 = reshape(shape = shape_14, x = scatter_2)[name = tensor("reshape_14")]; + tensor var_759_begin_0 = const()[name = tensor("op_759_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_759_end_0 = const()[name = tensor("op_759_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_759_end_mask_0 = const()[name = tensor("op_759_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_759_squeeze_mask_0 = const()[name = tensor("op_759_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_759 = slice_by_index(begin = var_759_begin_0, end = var_759_end_0, end_mask = var_759_end_mask_0, squeeze_mask = var_759_squeeze_mask_0, x = reshape_14)[name = tensor("op_759")]; + tensor var_761_axis_0 = const()[name = tensor("op_761_axis_0"), val = tensor(1)]; + tensor var_761_mode_0 = const()[name = tensor("op_761_mode_0"), val = tensor("update")]; + tensor var_761_validate_indices_0 = const()[name = tensor("op_761_validate_indices_0"), val = tensor(false)]; + tensor var_761 = scatter_along_axis(axis = var_761_axis_0, data = var_759, indices = write_indices_3, mode = var_761_mode_0, updates = v_3, validate_indices = var_761_validate_indices_0)[name = tensor("op_761")]; + tensor concat_11 = const()[name = tensor("concat_11"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_12 = const()[name = tensor("concat_12"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_15 = const()[name = tensor("shape_15"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_3 = const()[name = tensor("reduce_prod_3"), val = tensor(1048576)]; + tensor range_1d_3_start_0 = const()[name = tensor("range_1d_3_start_0"), val = tensor(0)]; + tensor range_1d_3_step_0 = const()[name = tensor("range_1d_3_step_0"), val = tensor(1)]; + tensor range_1d_3 = range_1d(end = reduce_prod_3, start = range_1d_3_start_0, step = range_1d_3_step_0)[name = tensor("range_1d_3")]; + tensor reshape_15 = reshape(shape = shape_15, x = range_1d_3)[name = tensor("reshape_15")]; + tensor slice_by_index_3 = slice_by_index(begin = concat_11, begin_mask = new_cache_3_internal_tensor_assign_2_begin_mask_0, end = concat_12, end_mask = new_cache_3_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_2_stride_0, x = reshape_15)[name = tensor("slice_by_index_3")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([-1])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = slice_by_index_3)[name = tensor("reshape_16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([-1])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = var_761)[name = tensor("reshape_17")]; + tensor reshape_18_shape_0 = const()[name = tensor("reshape_18_shape_0"), val = tensor([-1])]; + tensor reshape_18 = reshape(shape = reshape_18_shape_0, x = reshape_14)[name = tensor("reshape_18")]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3_validate_indices_0 = const()[name = tensor("scatter_3_validate_indices_0"), val = tensor(false)]; + tensor scatter_3 = scatter(axis = scatter_3_axis_0, data = reshape_18, indices = reshape_16, mode = scatter_3_mode_0, updates = reshape_17, validate_indices = scatter_3_validate_indices_0)[name = tensor("scatter_3")]; + tensor new_cache_3_internal_tensor_assign_2 = reshape(shape = shape_15, x = scatter_3)[name = tensor("reshape_19")]; + tensor keys_7_begin_0 = const()[name = tensor("keys_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_7_end_0 = const()[name = tensor("keys_7_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_7_end_mask_0 = const()[name = tensor("keys_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_7_squeeze_mask_0 = const()[name = tensor("keys_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_7 = slice_by_index(begin = keys_7_begin_0, end = keys_7_end_0, end_mask = keys_7_end_mask_0, squeeze_mask = keys_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("keys_7")]; + tensor values_7_begin_0 = const()[name = tensor("values_7_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_7_end_0 = const()[name = tensor("values_7_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_7_end_mask_0 = const()[name = tensor("values_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_7_squeeze_mask_0 = const()[name = tensor("values_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_7 = slice_by_index(begin = values_7_begin_0, end = values_7_end_0, end_mask = values_7_end_mask_0, squeeze_mask = values_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("values_7")]; + tensor var_773 = not_equal(x = keys_7, y = keys_7)[name = tensor("op_773")]; + tensor keys_9 = select(a = var_360, b = keys_7, cond = var_773)[name = tensor("keys_9")]; + tensor var_781 = not_equal(x = values_7, y = values_7)[name = tensor("op_781")]; + tensor values_9 = select(a = var_360, b = values_7, cond = var_781)[name = tensor("values_9")]; + tensor var_805 = const()[name = tensor("op_805"), val = tensor([0, 2, 1, 3])]; + tensor var_818 = const()[name = tensor("op_818"), val = tensor([1, 1, 1])]; + tensor var_819 = reshape(shape = var_818, x = position1)[name = tensor("op_819")]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor(0x1p+0)]; + tensor valid_len_3 = add(x = var_819, y = var_836)[name = tensor("valid_len_3")]; + tensor valid_mask_3 = less(x = k_positions_1_promoted, y = valid_len_3)[name = tensor("valid_mask_3")]; + tensor causal_mask_3 = less_equal(x = k_positions_1_promoted, y = var_819)[name = tensor("causal_mask_3")]; + tensor attn_mask_5 = logical_and(x = valid_mask_3, y = causal_mask_3)[name = tensor("attn_mask_5")]; + tensor attn_mask_7_axes_0 = const()[name = tensor("attn_mask_7_axes_0"), val = tensor([1])]; + tensor attn_mask_7 = expand_dims(axes = attn_mask_7_axes_0, x = attn_mask_5)[name = tensor("attn_mask_7")]; + tensor var_848 = const()[name = tensor("op_848"), val = tensor([0x1.fffe5cp-4])]; + tensor var_854_transpose_x_0 = const()[name = tensor("op_854_transpose_x_0"), val = tensor(false)]; + tensor var_854_transpose_y_0 = const()[name = tensor("op_854_transpose_y_0"), val = tensor(false)]; + tensor transpose_20_perm_0 = const()[name = tensor("transpose_20_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_21_perm_0 = const()[name = tensor("transpose_21_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_21 = transpose(perm = transpose_21_perm_0, x = keys_9)[name = tensor("transpose_47")]; + tensor transpose_20 = transpose(perm = transpose_20_perm_0, x = q_9)[name = tensor("transpose_48")]; + tensor var_854 = matmul(transpose_x = var_854_transpose_x_0, transpose_y = var_854_transpose_y_0, x = transpose_20, y = transpose_21)[name = tensor("op_854")]; + tensor attn_weights_7 = mul(x = var_854, y = var_848)[name = tensor("attn_weights_7")]; + tensor var_856 = logical_not(x = attn_mask_7)[name = tensor("op_856")]; + tensor var_857 = const()[name = tensor("op_857"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_9 = select(a = var_857, b = attn_weights_7, cond = var_856)[name = tensor("attn_weights_9")]; + tensor var_859 = const()[name = tensor("op_859"), val = tensor(-1)]; + tensor attn_weights_11 = softmax(axis = var_859, x = attn_weights_9)[name = tensor("attn_weights_11")]; + tensor attn_output_3_transpose_x_0 = const()[name = tensor("attn_output_3_transpose_x_0"), val = tensor(false)]; + tensor attn_output_3_transpose_y_0 = const()[name = tensor("attn_output_3_transpose_y_0"), val = tensor(false)]; + tensor values_11 = transpose(perm = var_805, x = values_9)[name = tensor("transpose_49")]; + tensor attn_output_3 = matmul(transpose_x = attn_output_3_transpose_x_0, transpose_y = attn_output_3_transpose_y_0, x = attn_weights_11, y = values_11)[name = tensor("attn_output_3")]; + tensor var_867 = const()[name = tensor("op_867"), val = tensor([0, 2, 1, 3])]; + tensor var_870 = const()[name = tensor("op_870"), val = tensor([1, 1, 1024])]; + tensor var_868 = transpose(perm = var_867, x = attn_output_3)[name = tensor("transpose_46")]; + tensor input_15 = reshape(shape = var_870, x = var_868)[name = tensor("input_15")]; + tensor attn_out_3 = linear(bias = linear_0_bias_0, weight = attn1_out_proj_weight, x = input_15)[name = tensor("linear_6")]; + tensor var_876 = const()[name = tensor("op_876"), val = tensor(0x1p+0)]; + tensor var_877 = add(x = position1, y = var_876)[name = tensor("op_877")]; + tensor input_17 = add(x = input_13, y = attn_out_3)[name = tensor("input_17")]; + tensor var_881 = const()[name = tensor("op_881"), val = tensor(0x1.4f8b58p-17)]; + tensor input_19_axes_0 = const()[name = tensor("input_19_axes_0"), val = tensor([-1])]; + tensor input_19 = layer_norm(axes = input_19_axes_0, beta = norm1_2_bias, epsilon = var_881, gamma = norm1_2_weight, x = input_17)[name = tensor("input_19")]; + tensor var_889 = linear(bias = linear_3_bias_0, weight = linear1_1_weight, x = input_19)[name = tensor("linear_7")]; + tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("EXACT")]; + tensor input_21 = gelu(mode = input_21_mode_0, x = var_889)[name = tensor("input_21")]; + tensor ffn_out_3 = linear(bias = linear_0_bias_0, weight = linear1_2_weight, x = input_21)[name = tensor("linear_8")]; + tensor input_23 = add(x = input_17, y = ffn_out_3)[name = tensor("input_23")]; + tensor var_898 = const()[name = tensor("op_898"), val = tensor(0x1.4f8b58p-17)]; + tensor x_5_axes_0 = const()[name = tensor("x_5_axes_0"), val = tensor([-1])]; + tensor x_5 = layer_norm(axes = x_5_axes_0, beta = norm2_1_bias, epsilon = var_898, gamma = norm2_1_weight, x = input_23)[name = tensor("x_5")]; + tensor var_930 = linear(bias = linear_1_bias_0, weight = attn2_in_proj_weight, x = x_5)[name = tensor("linear_9")]; + tensor var_934 = const()[name = tensor("op_934"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_5 = reshape(shape = var_934, x = var_930)[name = tensor("qkv_5")]; + tensor q_13_begin_0 = const()[name = tensor("q_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_13_end_0 = const()[name = tensor("q_13_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_13_end_mask_0 = const()[name = tensor("q_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_13_squeeze_mask_0 = const()[name = tensor("q_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_13 = slice_by_index(begin = q_13_begin_0, end = q_13_end_0, end_mask = q_13_end_mask_0, squeeze_mask = q_13_squeeze_mask_0, x = qkv_5)[name = tensor("q_13")]; + tensor k_9_begin_0 = const()[name = tensor("k_9_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_9_end_0 = const()[name = tensor("k_9_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_9_end_mask_0 = const()[name = tensor("k_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_9_squeeze_mask_0 = const()[name = tensor("k_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_9 = slice_by_index(begin = k_9_begin_0, end = k_9_end_0, end_mask = k_9_end_mask_0, squeeze_mask = k_9_squeeze_mask_0, x = qkv_5)[name = tensor("k_9")]; + tensor v_5_begin_0 = const()[name = tensor("v_5_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_5_end_0 = const()[name = tensor("v_5_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_5_end_mask_0 = const()[name = tensor("v_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_5_squeeze_mask_0 = const()[name = tensor("v_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_5 = slice_by_index(begin = v_5_begin_0, end = v_5_end_0, end_mask = v_5_end_mask_0, squeeze_mask = v_5_squeeze_mask_0, x = qkv_5)[name = tensor("v_5")]; + tensor freqs_5 = const()[name = tensor("freqs_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304367616)))]; + tensor var_1038 = const()[name = tensor("op_1038"), val = tensor([1, 1, 1, 1])]; + tensor ts_17 = reshape(shape = var_1038, x = position2)[name = tensor("ts_17")]; + tensor var_1042 = const()[name = tensor("op_1042"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_5 = reshape(shape = var_1042, x = q_13)[name = tensor("q_complex_5")]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_5 = reshape(shape = var_1046, x = k_9)[name = tensor("k_complex_5")]; + tensor var_1050_begin_0 = const()[name = tensor("op_1050_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1050_end_0 = const()[name = tensor("op_1050_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1050_end_mask_0 = const()[name = tensor("op_1050_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1050_squeeze_mask_0 = const()[name = tensor("op_1050_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1050 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1050")]; + tensor var_1058_begin_0 = const()[name = tensor("op_1058_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1058_end_0 = const()[name = tensor("op_1058_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1058_end_mask_0 = const()[name = tensor("op_1058_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1058_squeeze_mask_0 = const()[name = tensor("op_1058_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1058 = slice_by_index(begin = var_1058_begin_0, end = var_1058_end_0, end_mask = var_1058_end_mask_0, squeeze_mask = var_1058_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1058")]; + tensor var_1066_begin_0 = const()[name = tensor("op_1066_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1066_end_0 = const()[name = tensor("op_1066_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1066_end_mask_0 = const()[name = tensor("op_1066_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1066_squeeze_mask_0 = const()[name = tensor("op_1066_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1066 = slice_by_index(begin = var_1066_begin_0, end = var_1066_end_0, end_mask = var_1066_end_mask_0, squeeze_mask = var_1066_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1066")]; + tensor var_1074_begin_0 = const()[name = tensor("op_1074_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1074_end_0 = const()[name = tensor("op_1074_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1074_end_mask_0 = const()[name = tensor("op_1074_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1074_squeeze_mask_0 = const()[name = tensor("op_1074_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1074 = slice_by_index(begin = var_1074_begin_0, end = var_1074_end_0, end_mask = var_1074_end_mask_0, squeeze_mask = var_1074_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1074")]; + tensor var_1080 = mul(x = freqs_5, y = ts_17)[name = tensor("op_1080")]; + tensor rotr_5 = cos(x = var_1080)[name = tensor("rotr_5")]; + tensor roti_5 = sin(x = var_1080)[name = tensor("roti_5")]; + tensor var_1084 = mul(x = var_1050, y = rotr_5)[name = tensor("op_1084")]; + tensor var_1085 = mul(x = var_1058, y = roti_5)[name = tensor("op_1085")]; + tensor qor_9 = sub(x = var_1084, y = var_1085)[name = tensor("qor_9")]; + tensor var_1088 = mul(x = var_1050, y = roti_5)[name = tensor("op_1088")]; + tensor var_1089 = mul(x = var_1058, y = rotr_5)[name = tensor("op_1089")]; + tensor qoi_9 = add(x = var_1088, y = var_1089)[name = tensor("qoi_9")]; + tensor var_1092 = mul(x = var_1066, y = rotr_5)[name = tensor("op_1092")]; + tensor var_1093 = mul(x = var_1074, y = roti_5)[name = tensor("op_1093")]; + tensor kor_9 = sub(x = var_1092, y = var_1093)[name = tensor("kor_9")]; + tensor var_1096 = mul(x = var_1066, y = roti_5)[name = tensor("op_1096")]; + tensor var_1097 = mul(x = var_1074, y = rotr_5)[name = tensor("op_1097")]; + tensor koi_9 = add(x = var_1096, y = var_1097)[name = tensor("koi_9")]; + tensor qo_5_axis_0 = const()[name = tensor("qo_5_axis_0"), val = tensor(-1)]; + tensor qo_5 = stack(axis = qo_5_axis_0, values = (qor_9, qoi_9))[name = tensor("qo_5")]; + tensor ko_5_axis_0 = const()[name = tensor("ko_5_axis_0"), val = tensor(-1)]; + tensor ko_5 = stack(axis = ko_5_axis_0, values = (kor_9, koi_9))[name = tensor("ko_5")]; + tensor var_1126 = const()[name = tensor("op_1126"), val = tensor([1, 1, 16, 64])]; + tensor q_15 = reshape(shape = var_1126, x = qo_5)[name = tensor("q_15")]; + tensor var_1128 = const()[name = tensor("op_1128"), val = tensor([1, 1, 16, 64])]; + tensor k_11 = reshape(shape = var_1128, x = ko_5)[name = tensor("k_11")]; + tensor _inversed_1150_y_0 = const()[name = tensor("_inversed_1150_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1150 = mul(x = ts_17, y = _inversed_1150_y_0)[name = tensor("_inversed_1150")]; + tensor var_1151 = floor(x = _inversed_1150)[name = tensor("op_1151")]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor(0x1p+9)]; + tensor var_1153 = mul(x = var_1151, y = var_1152)[name = tensor("op_1153")]; + tensor write_indices_float_11 = sub(x = ts_17, y = var_1153)[name = tensor("write_indices_float_11")]; + tensor var_1160_dtype_0 = const()[name = tensor("op_1160_dtype_0"), val = tensor("int32")]; + tensor write_indices_5_reps_0 = const()[name = tensor("write_indices_5_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1160 = cast(dtype = var_1160_dtype_0, x = write_indices_float_11)[name = tensor("cast_111")]; + tensor write_indices_5 = tile(reps = write_indices_5_reps_0, x = var_1160)[name = tensor("write_indices_5")]; + tensor var_1168_begin_0 = const()[name = tensor("op_1168_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1168_end_0 = const()[name = tensor("op_1168_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1168_end_mask_0 = const()[name = tensor("op_1168_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1168_squeeze_mask_0 = const()[name = tensor("op_1168_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1168 = slice_by_index(begin = var_1168_begin_0, end = var_1168_end_0, end_mask = var_1168_end_mask_0, squeeze_mask = var_1168_squeeze_mask_0, x = cache2)[name = tensor("op_1168")]; + tensor var_1170_axis_0 = const()[name = tensor("op_1170_axis_0"), val = tensor(1)]; + tensor var_1170_mode_0 = const()[name = tensor("op_1170_mode_0"), val = tensor("update")]; + tensor var_1170_validate_indices_0 = const()[name = tensor("op_1170_validate_indices_0"), val = tensor(false)]; + tensor var_1170 = scatter_along_axis(axis = var_1170_axis_0, data = var_1168, indices = write_indices_5, mode = var_1170_mode_0, updates = k_11, validate_indices = var_1170_validate_indices_0)[name = tensor("op_1170")]; + tensor concat_16 = const()[name = tensor("concat_16"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_16 = const()[name = tensor("shape_16"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_4 = const()[name = tensor("reduce_prod_4"), val = tensor(1048576)]; + tensor range_1d_4_start_0 = const()[name = tensor("range_1d_4_start_0"), val = tensor(0)]; + tensor range_1d_4_step_0 = const()[name = tensor("range_1d_4_step_0"), val = tensor(1)]; + tensor range_1d_4 = range_1d(end = reduce_prod_4, start = range_1d_4_start_0, step = range_1d_4_step_0)[name = tensor("range_1d_4")]; + tensor reshape_20 = reshape(shape = shape_16, x = range_1d_4)[name = tensor("reshape_20")]; + tensor slice_by_index_4 = slice_by_index(begin = concat_16, begin_mask = new_cache_5_internal_tensor_assign_1_begin_mask_0, end = concat_17, end_mask = new_cache_5_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_1_stride_0, x = reshape_20)[name = tensor("slice_by_index_4")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([-1])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = slice_by_index_4)[name = tensor("reshape_21")]; + tensor reshape_22_shape_0 = const()[name = tensor("reshape_22_shape_0"), val = tensor([-1])]; + tensor reshape_22 = reshape(shape = reshape_22_shape_0, x = var_1170)[name = tensor("reshape_22")]; + tensor reshape_23_shape_0 = const()[name = tensor("reshape_23_shape_0"), val = tensor([-1])]; + tensor reshape_23 = reshape(shape = reshape_23_shape_0, x = cache2)[name = tensor("reshape_23")]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4_validate_indices_0 = const()[name = tensor("scatter_4_validate_indices_0"), val = tensor(false)]; + tensor scatter_4 = scatter(axis = scatter_4_axis_0, data = reshape_23, indices = reshape_21, mode = scatter_4_mode_0, updates = reshape_22, validate_indices = scatter_4_validate_indices_0)[name = tensor("scatter_4")]; + tensor reshape_24 = reshape(shape = shape_16, x = scatter_4)[name = tensor("reshape_24")]; + tensor var_1178_begin_0 = const()[name = tensor("op_1178_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1178_end_0 = const()[name = tensor("op_1178_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1178_end_mask_0 = const()[name = tensor("op_1178_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1178_squeeze_mask_0 = const()[name = tensor("op_1178_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1178 = slice_by_index(begin = var_1178_begin_0, end = var_1178_end_0, end_mask = var_1178_end_mask_0, squeeze_mask = var_1178_squeeze_mask_0, x = reshape_24)[name = tensor("op_1178")]; + tensor var_1180_axis_0 = const()[name = tensor("op_1180_axis_0"), val = tensor(1)]; + tensor var_1180_mode_0 = const()[name = tensor("op_1180_mode_0"), val = tensor("update")]; + tensor var_1180_validate_indices_0 = const()[name = tensor("op_1180_validate_indices_0"), val = tensor(false)]; + tensor var_1180 = scatter_along_axis(axis = var_1180_axis_0, data = var_1178, indices = write_indices_5, mode = var_1180_mode_0, updates = v_5, validate_indices = var_1180_validate_indices_0)[name = tensor("op_1180")]; + tensor concat_18 = const()[name = tensor("concat_18"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_19 = const()[name = tensor("concat_19"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_17 = const()[name = tensor("shape_17"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_5 = const()[name = tensor("reduce_prod_5"), val = tensor(1048576)]; + tensor range_1d_5_start_0 = const()[name = tensor("range_1d_5_start_0"), val = tensor(0)]; + tensor range_1d_5_step_0 = const()[name = tensor("range_1d_5_step_0"), val = tensor(1)]; + tensor range_1d_5 = range_1d(end = reduce_prod_5, start = range_1d_5_start_0, step = range_1d_5_step_0)[name = tensor("range_1d_5")]; + tensor reshape_25 = reshape(shape = shape_17, x = range_1d_5)[name = tensor("reshape_25")]; + tensor slice_by_index_5 = slice_by_index(begin = concat_18, begin_mask = new_cache_5_internal_tensor_assign_2_begin_mask_0, end = concat_19, end_mask = new_cache_5_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_2_stride_0, x = reshape_25)[name = tensor("slice_by_index_5")]; + tensor reshape_26_shape_0 = const()[name = tensor("reshape_26_shape_0"), val = tensor([-1])]; + tensor reshape_26 = reshape(shape = reshape_26_shape_0, x = slice_by_index_5)[name = tensor("reshape_26")]; + tensor reshape_27_shape_0 = const()[name = tensor("reshape_27_shape_0"), val = tensor([-1])]; + tensor reshape_27 = reshape(shape = reshape_27_shape_0, x = var_1180)[name = tensor("reshape_27")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([-1])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = reshape_24)[name = tensor("reshape_28")]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5_validate_indices_0 = const()[name = tensor("scatter_5_validate_indices_0"), val = tensor(false)]; + tensor scatter_5 = scatter(axis = scatter_5_axis_0, data = reshape_28, indices = reshape_26, mode = scatter_5_mode_0, updates = reshape_27, validate_indices = scatter_5_validate_indices_0)[name = tensor("scatter_5")]; + tensor new_cache_5_internal_tensor_assign_2 = reshape(shape = shape_17, x = scatter_5)[name = tensor("reshape_29")]; + tensor keys_13_begin_0 = const()[name = tensor("keys_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_13_end_0 = const()[name = tensor("keys_13_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_13_end_mask_0 = const()[name = tensor("keys_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_13_squeeze_mask_0 = const()[name = tensor("keys_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_13 = slice_by_index(begin = keys_13_begin_0, end = keys_13_end_0, end_mask = keys_13_end_mask_0, squeeze_mask = keys_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("keys_13")]; + tensor values_13_begin_0 = const()[name = tensor("values_13_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_13_end_0 = const()[name = tensor("values_13_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_13_end_mask_0 = const()[name = tensor("values_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_13_squeeze_mask_0 = const()[name = tensor("values_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_13 = slice_by_index(begin = values_13_begin_0, end = values_13_end_0, end_mask = values_13_end_mask_0, squeeze_mask = values_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("values_13")]; + tensor var_1192 = not_equal(x = keys_13, y = keys_13)[name = tensor("op_1192")]; + tensor keys_15 = select(a = var_360, b = keys_13, cond = var_1192)[name = tensor("keys_15")]; + tensor var_1200 = not_equal(x = values_13, y = values_13)[name = tensor("op_1200")]; + tensor values_15 = select(a = var_360, b = values_13, cond = var_1200)[name = tensor("values_15")]; + tensor var_1224 = const()[name = tensor("op_1224"), val = tensor([0, 2, 1, 3])]; + tensor var_1237 = const()[name = tensor("op_1237"), val = tensor([1, 1, 1])]; + tensor var_1238 = reshape(shape = var_1237, x = position2)[name = tensor("op_1238")]; + tensor var_1255 = const()[name = tensor("op_1255"), val = tensor(0x1p+0)]; + tensor valid_len_5 = add(x = var_1238, y = var_1255)[name = tensor("valid_len_5")]; + tensor valid_mask_5 = less(x = k_positions_1_promoted, y = valid_len_5)[name = tensor("valid_mask_5")]; + tensor causal_mask_5 = less_equal(x = k_positions_1_promoted, y = var_1238)[name = tensor("causal_mask_5")]; + tensor attn_mask_9 = logical_and(x = valid_mask_5, y = causal_mask_5)[name = tensor("attn_mask_9")]; + tensor attn_mask_11_axes_0 = const()[name = tensor("attn_mask_11_axes_0"), val = tensor([1])]; + tensor attn_mask_11 = expand_dims(axes = attn_mask_11_axes_0, x = attn_mask_9)[name = tensor("attn_mask_11")]; + tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1273_transpose_x_0 = const()[name = tensor("op_1273_transpose_x_0"), val = tensor(false)]; + tensor var_1273_transpose_y_0 = const()[name = tensor("op_1273_transpose_y_0"), val = tensor(false)]; + tensor transpose_22_perm_0 = const()[name = tensor("transpose_22_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_23_perm_0 = const()[name = tensor("transpose_23_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_23 = transpose(perm = transpose_23_perm_0, x = keys_15)[name = tensor("transpose_43")]; + tensor transpose_22 = transpose(perm = transpose_22_perm_0, x = q_15)[name = tensor("transpose_44")]; + tensor var_1273 = matmul(transpose_x = var_1273_transpose_x_0, transpose_y = var_1273_transpose_y_0, x = transpose_22, y = transpose_23)[name = tensor("op_1273")]; + tensor attn_weights_13 = mul(x = var_1273, y = var_1267)[name = tensor("attn_weights_13")]; + tensor var_1275 = logical_not(x = attn_mask_11)[name = tensor("op_1275")]; + tensor var_1276 = const()[name = tensor("op_1276"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_15 = select(a = var_1276, b = attn_weights_13, cond = var_1275)[name = tensor("attn_weights_15")]; + tensor var_1278 = const()[name = tensor("op_1278"), val = tensor(-1)]; + tensor attn_weights_17 = softmax(axis = var_1278, x = attn_weights_15)[name = tensor("attn_weights_17")]; + tensor attn_output_5_transpose_x_0 = const()[name = tensor("attn_output_5_transpose_x_0"), val = tensor(false)]; + tensor attn_output_5_transpose_y_0 = const()[name = tensor("attn_output_5_transpose_y_0"), val = tensor(false)]; + tensor values_17 = transpose(perm = var_1224, x = values_15)[name = tensor("transpose_45")]; + tensor attn_output_5 = matmul(transpose_x = attn_output_5_transpose_x_0, transpose_y = attn_output_5_transpose_y_0, x = attn_weights_17, y = values_17)[name = tensor("attn_output_5")]; + tensor var_1286 = const()[name = tensor("op_1286"), val = tensor([0, 2, 1, 3])]; + tensor var_1289 = const()[name = tensor("op_1289"), val = tensor([1, 1, 1024])]; + tensor var_1287 = transpose(perm = var_1286, x = attn_output_5)[name = tensor("transpose_42")]; + tensor input_25 = reshape(shape = var_1289, x = var_1287)[name = tensor("input_25")]; + tensor attn_out_5 = linear(bias = linear_0_bias_0, weight = attn2_out_proj_weight, x = input_25)[name = tensor("linear_10")]; + tensor var_1295 = const()[name = tensor("op_1295"), val = tensor(0x1p+0)]; + tensor var_1296 = add(x = position2, y = var_1295)[name = tensor("op_1296")]; + tensor input_27 = add(x = input_23, y = attn_out_5)[name = tensor("input_27")]; + tensor var_1300 = const()[name = tensor("op_1300"), val = tensor(0x1.4f8b58p-17)]; + tensor input_29_axes_0 = const()[name = tensor("input_29_axes_0"), val = tensor([-1])]; + tensor input_29 = layer_norm(axes = input_29_axes_0, beta = norm2_2_bias, epsilon = var_1300, gamma = norm2_2_weight, x = input_27)[name = tensor("input_29")]; + tensor var_1308 = linear(bias = linear_3_bias_0, weight = linear2_1_weight, x = input_29)[name = tensor("linear_11")]; + tensor input_31_mode_0 = const()[name = tensor("input_31_mode_0"), val = tensor("EXACT")]; + tensor input_31 = gelu(mode = input_31_mode_0, x = var_1308)[name = tensor("input_31")]; + tensor ffn_out_5 = linear(bias = linear_0_bias_0, weight = linear2_2_weight, x = input_31)[name = tensor("linear_12")]; + tensor input_33 = add(x = input_27, y = ffn_out_5)[name = tensor("input_33")]; + tensor var_1317 = const()[name = tensor("op_1317"), val = tensor(0x1.4f8b58p-17)]; + tensor x_7_axes_0 = const()[name = tensor("x_7_axes_0"), val = tensor([-1])]; + tensor x_7 = layer_norm(axes = x_7_axes_0, beta = norm3_1_bias, epsilon = var_1317, gamma = norm3_1_weight, x = input_33)[name = tensor("x_7")]; + tensor var_1349 = linear(bias = linear_1_bias_0, weight = attn3_in_proj_weight, x = x_7)[name = tensor("linear_13")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_7 = reshape(shape = var_1353, x = var_1349)[name = tensor("qkv_7")]; + tensor q_19_begin_0 = const()[name = tensor("q_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_19_end_0 = const()[name = tensor("q_19_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_19_end_mask_0 = const()[name = tensor("q_19_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_19_squeeze_mask_0 = const()[name = tensor("q_19_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_19 = slice_by_index(begin = q_19_begin_0, end = q_19_end_0, end_mask = q_19_end_mask_0, squeeze_mask = q_19_squeeze_mask_0, x = qkv_7)[name = tensor("q_19")]; + tensor k_13_begin_0 = const()[name = tensor("k_13_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_13_end_0 = const()[name = tensor("k_13_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_13_end_mask_0 = const()[name = tensor("k_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_13_squeeze_mask_0 = const()[name = tensor("k_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_13 = slice_by_index(begin = k_13_begin_0, end = k_13_end_0, end_mask = k_13_end_mask_0, squeeze_mask = k_13_squeeze_mask_0, x = qkv_7)[name = tensor("k_13")]; + tensor v_7_begin_0 = const()[name = tensor("v_7_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_7_end_0 = const()[name = tensor("v_7_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_7_end_mask_0 = const()[name = tensor("v_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_7_squeeze_mask_0 = const()[name = tensor("v_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_7 = slice_by_index(begin = v_7_begin_0, end = v_7_end_0, end_mask = v_7_end_mask_0, squeeze_mask = v_7_squeeze_mask_0, x = qkv_7)[name = tensor("v_7")]; + tensor freqs_7 = const()[name = tensor("freqs_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304367808)))]; + tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 1, 1, 1])]; + tensor ts_23 = reshape(shape = var_1457, x = position3)[name = tensor("ts_23")]; + tensor var_1461 = const()[name = tensor("op_1461"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_7 = reshape(shape = var_1461, x = q_19)[name = tensor("q_complex_7")]; + tensor var_1465 = const()[name = tensor("op_1465"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_7 = reshape(shape = var_1465, x = k_13)[name = tensor("k_complex_7")]; + tensor var_1469_begin_0 = const()[name = tensor("op_1469_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1469_end_0 = const()[name = tensor("op_1469_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1469_end_mask_0 = const()[name = tensor("op_1469_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1469_squeeze_mask_0 = const()[name = tensor("op_1469_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1469 = slice_by_index(begin = var_1469_begin_0, end = var_1469_end_0, end_mask = var_1469_end_mask_0, squeeze_mask = var_1469_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1469")]; + tensor var_1477_begin_0 = const()[name = tensor("op_1477_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1477_end_0 = const()[name = tensor("op_1477_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1477_end_mask_0 = const()[name = tensor("op_1477_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1477_squeeze_mask_0 = const()[name = tensor("op_1477_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1477 = slice_by_index(begin = var_1477_begin_0, end = var_1477_end_0, end_mask = var_1477_end_mask_0, squeeze_mask = var_1477_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1477")]; + tensor var_1485_begin_0 = const()[name = tensor("op_1485_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1485_end_0 = const()[name = tensor("op_1485_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1485_end_mask_0 = const()[name = tensor("op_1485_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1485_squeeze_mask_0 = const()[name = tensor("op_1485_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1485 = slice_by_index(begin = var_1485_begin_0, end = var_1485_end_0, end_mask = var_1485_end_mask_0, squeeze_mask = var_1485_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1485")]; + tensor var_1493_begin_0 = const()[name = tensor("op_1493_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1493_end_0 = const()[name = tensor("op_1493_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1493_end_mask_0 = const()[name = tensor("op_1493_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1493_squeeze_mask_0 = const()[name = tensor("op_1493_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1493 = slice_by_index(begin = var_1493_begin_0, end = var_1493_end_0, end_mask = var_1493_end_mask_0, squeeze_mask = var_1493_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1493")]; + tensor var_1499 = mul(x = freqs_7, y = ts_23)[name = tensor("op_1499")]; + tensor rotr_7 = cos(x = var_1499)[name = tensor("rotr_7")]; + tensor roti_7 = sin(x = var_1499)[name = tensor("roti_7")]; + tensor var_1503 = mul(x = var_1469, y = rotr_7)[name = tensor("op_1503")]; + tensor var_1504 = mul(x = var_1477, y = roti_7)[name = tensor("op_1504")]; + tensor qor_13 = sub(x = var_1503, y = var_1504)[name = tensor("qor_13")]; + tensor var_1507 = mul(x = var_1469, y = roti_7)[name = tensor("op_1507")]; + tensor var_1508 = mul(x = var_1477, y = rotr_7)[name = tensor("op_1508")]; + tensor qoi_13 = add(x = var_1507, y = var_1508)[name = tensor("qoi_13")]; + tensor var_1511 = mul(x = var_1485, y = rotr_7)[name = tensor("op_1511")]; + tensor var_1512 = mul(x = var_1493, y = roti_7)[name = tensor("op_1512")]; + tensor kor_13 = sub(x = var_1511, y = var_1512)[name = tensor("kor_13")]; + tensor var_1515 = mul(x = var_1485, y = roti_7)[name = tensor("op_1515")]; + tensor var_1516 = mul(x = var_1493, y = rotr_7)[name = tensor("op_1516")]; + tensor koi_13 = add(x = var_1515, y = var_1516)[name = tensor("koi_13")]; + tensor qo_7_axis_0 = const()[name = tensor("qo_7_axis_0"), val = tensor(-1)]; + tensor qo_7 = stack(axis = qo_7_axis_0, values = (qor_13, qoi_13))[name = tensor("qo_7")]; + tensor ko_7_axis_0 = const()[name = tensor("ko_7_axis_0"), val = tensor(-1)]; + tensor ko_7 = stack(axis = ko_7_axis_0, values = (kor_13, koi_13))[name = tensor("ko_7")]; + tensor var_1545 = const()[name = tensor("op_1545"), val = tensor([1, 1, 16, 64])]; + tensor q_21 = reshape(shape = var_1545, x = qo_7)[name = tensor("q_21")]; + tensor var_1547 = const()[name = tensor("op_1547"), val = tensor([1, 1, 16, 64])]; + tensor k_15 = reshape(shape = var_1547, x = ko_7)[name = tensor("k_15")]; + tensor _inversed_1569_y_0 = const()[name = tensor("_inversed_1569_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1569 = mul(x = ts_23, y = _inversed_1569_y_0)[name = tensor("_inversed_1569")]; + tensor var_1570 = floor(x = _inversed_1569)[name = tensor("op_1570")]; + tensor var_1571 = const()[name = tensor("op_1571"), val = tensor(0x1p+9)]; + tensor var_1572 = mul(x = var_1570, y = var_1571)[name = tensor("op_1572")]; + tensor write_indices_float_15 = sub(x = ts_23, y = var_1572)[name = tensor("write_indices_float_15")]; + tensor var_1579_dtype_0 = const()[name = tensor("op_1579_dtype_0"), val = tensor("int32")]; + tensor write_indices_7_reps_0 = const()[name = tensor("write_indices_7_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1579 = cast(dtype = var_1579_dtype_0, x = write_indices_float_15)[name = tensor("cast_110")]; + tensor write_indices_7 = tile(reps = write_indices_7_reps_0, x = var_1579)[name = tensor("write_indices_7")]; + tensor var_1587_begin_0 = const()[name = tensor("op_1587_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1587_end_0 = const()[name = tensor("op_1587_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1587_end_mask_0 = const()[name = tensor("op_1587_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1587_squeeze_mask_0 = const()[name = tensor("op_1587_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1587 = slice_by_index(begin = var_1587_begin_0, end = var_1587_end_0, end_mask = var_1587_end_mask_0, squeeze_mask = var_1587_squeeze_mask_0, x = cache3)[name = tensor("op_1587")]; + tensor var_1589_axis_0 = const()[name = tensor("op_1589_axis_0"), val = tensor(1)]; + tensor var_1589_mode_0 = const()[name = tensor("op_1589_mode_0"), val = tensor("update")]; + tensor var_1589_validate_indices_0 = const()[name = tensor("op_1589_validate_indices_0"), val = tensor(false)]; + tensor var_1589 = scatter_along_axis(axis = var_1589_axis_0, data = var_1587, indices = write_indices_7, mode = var_1589_mode_0, updates = k_15, validate_indices = var_1589_validate_indices_0)[name = tensor("op_1589")]; + tensor concat_23 = const()[name = tensor("concat_23"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_24 = const()[name = tensor("concat_24"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_18 = const()[name = tensor("shape_18"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_6 = const()[name = tensor("reduce_prod_6"), val = tensor(1048576)]; + tensor range_1d_6_start_0 = const()[name = tensor("range_1d_6_start_0"), val = tensor(0)]; + tensor range_1d_6_step_0 = const()[name = tensor("range_1d_6_step_0"), val = tensor(1)]; + tensor range_1d_6 = range_1d(end = reduce_prod_6, start = range_1d_6_start_0, step = range_1d_6_step_0)[name = tensor("range_1d_6")]; + tensor reshape_30 = reshape(shape = shape_18, x = range_1d_6)[name = tensor("reshape_30")]; + tensor slice_by_index_6 = slice_by_index(begin = concat_23, begin_mask = new_cache_7_internal_tensor_assign_1_begin_mask_0, end = concat_24, end_mask = new_cache_7_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_1_stride_0, x = reshape_30)[name = tensor("slice_by_index_6")]; + tensor reshape_31_shape_0 = const()[name = tensor("reshape_31_shape_0"), val = tensor([-1])]; + tensor reshape_31 = reshape(shape = reshape_31_shape_0, x = slice_by_index_6)[name = tensor("reshape_31")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([-1])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = var_1589)[name = tensor("reshape_32")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([-1])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = cache3)[name = tensor("reshape_33")]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6_validate_indices_0 = const()[name = tensor("scatter_6_validate_indices_0"), val = tensor(false)]; + tensor scatter_6 = scatter(axis = scatter_6_axis_0, data = reshape_33, indices = reshape_31, mode = scatter_6_mode_0, updates = reshape_32, validate_indices = scatter_6_validate_indices_0)[name = tensor("scatter_6")]; + tensor reshape_34 = reshape(shape = shape_18, x = scatter_6)[name = tensor("reshape_34")]; + tensor var_1597_begin_0 = const()[name = tensor("op_1597_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1597_end_0 = const()[name = tensor("op_1597_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1597_end_mask_0 = const()[name = tensor("op_1597_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1597_squeeze_mask_0 = const()[name = tensor("op_1597_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1597 = slice_by_index(begin = var_1597_begin_0, end = var_1597_end_0, end_mask = var_1597_end_mask_0, squeeze_mask = var_1597_squeeze_mask_0, x = reshape_34)[name = tensor("op_1597")]; + tensor var_1599_axis_0 = const()[name = tensor("op_1599_axis_0"), val = tensor(1)]; + tensor var_1599_mode_0 = const()[name = tensor("op_1599_mode_0"), val = tensor("update")]; + tensor var_1599_validate_indices_0 = const()[name = tensor("op_1599_validate_indices_0"), val = tensor(false)]; + tensor var_1599 = scatter_along_axis(axis = var_1599_axis_0, data = var_1597, indices = write_indices_7, mode = var_1599_mode_0, updates = v_7, validate_indices = var_1599_validate_indices_0)[name = tensor("op_1599")]; + tensor concat_25 = const()[name = tensor("concat_25"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_26 = const()[name = tensor("concat_26"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_19 = const()[name = tensor("shape_19"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_7 = const()[name = tensor("reduce_prod_7"), val = tensor(1048576)]; + tensor range_1d_7_start_0 = const()[name = tensor("range_1d_7_start_0"), val = tensor(0)]; + tensor range_1d_7_step_0 = const()[name = tensor("range_1d_7_step_0"), val = tensor(1)]; + tensor range_1d_7 = range_1d(end = reduce_prod_7, start = range_1d_7_start_0, step = range_1d_7_step_0)[name = tensor("range_1d_7")]; + tensor reshape_35 = reshape(shape = shape_19, x = range_1d_7)[name = tensor("reshape_35")]; + tensor slice_by_index_7 = slice_by_index(begin = concat_25, begin_mask = new_cache_7_internal_tensor_assign_2_begin_mask_0, end = concat_26, end_mask = new_cache_7_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_2_stride_0, x = reshape_35)[name = tensor("slice_by_index_7")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([-1])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = slice_by_index_7)[name = tensor("reshape_36")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([-1])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = var_1599)[name = tensor("reshape_37")]; + tensor reshape_38_shape_0 = const()[name = tensor("reshape_38_shape_0"), val = tensor([-1])]; + tensor reshape_38 = reshape(shape = reshape_38_shape_0, x = reshape_34)[name = tensor("reshape_38")]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7_validate_indices_0 = const()[name = tensor("scatter_7_validate_indices_0"), val = tensor(false)]; + tensor scatter_7 = scatter(axis = scatter_7_axis_0, data = reshape_38, indices = reshape_36, mode = scatter_7_mode_0, updates = reshape_37, validate_indices = scatter_7_validate_indices_0)[name = tensor("scatter_7")]; + tensor new_cache_7_internal_tensor_assign_2 = reshape(shape = shape_19, x = scatter_7)[name = tensor("reshape_39")]; + tensor keys_19_begin_0 = const()[name = tensor("keys_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_19_end_0 = const()[name = tensor("keys_19_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_19_end_mask_0 = const()[name = tensor("keys_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_19_squeeze_mask_0 = const()[name = tensor("keys_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_19 = slice_by_index(begin = keys_19_begin_0, end = keys_19_end_0, end_mask = keys_19_end_mask_0, squeeze_mask = keys_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("keys_19")]; + tensor values_19_begin_0 = const()[name = tensor("values_19_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_19_end_0 = const()[name = tensor("values_19_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_19_end_mask_0 = const()[name = tensor("values_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_19_squeeze_mask_0 = const()[name = tensor("values_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_19 = slice_by_index(begin = values_19_begin_0, end = values_19_end_0, end_mask = values_19_end_mask_0, squeeze_mask = values_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("values_19")]; + tensor var_1611 = not_equal(x = keys_19, y = keys_19)[name = tensor("op_1611")]; + tensor keys_21 = select(a = var_360, b = keys_19, cond = var_1611)[name = tensor("keys_21")]; + tensor var_1619 = not_equal(x = values_19, y = values_19)[name = tensor("op_1619")]; + tensor values_21 = select(a = var_360, b = values_19, cond = var_1619)[name = tensor("values_21")]; + tensor var_1643 = const()[name = tensor("op_1643"), val = tensor([0, 2, 1, 3])]; + tensor var_1656 = const()[name = tensor("op_1656"), val = tensor([1, 1, 1])]; + tensor var_1657 = reshape(shape = var_1656, x = position3)[name = tensor("op_1657")]; + tensor var_1674 = const()[name = tensor("op_1674"), val = tensor(0x1p+0)]; + tensor valid_len_7 = add(x = var_1657, y = var_1674)[name = tensor("valid_len_7")]; + tensor valid_mask_7 = less(x = k_positions_1_promoted, y = valid_len_7)[name = tensor("valid_mask_7")]; + tensor causal_mask_7 = less_equal(x = k_positions_1_promoted, y = var_1657)[name = tensor("causal_mask_7")]; + tensor attn_mask_13 = logical_and(x = valid_mask_7, y = causal_mask_7)[name = tensor("attn_mask_13")]; + tensor attn_mask_15_axes_0 = const()[name = tensor("attn_mask_15_axes_0"), val = tensor([1])]; + tensor attn_mask_15 = expand_dims(axes = attn_mask_15_axes_0, x = attn_mask_13)[name = tensor("attn_mask_15")]; + tensor var_1686 = const()[name = tensor("op_1686"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1692_transpose_x_0 = const()[name = tensor("op_1692_transpose_x_0"), val = tensor(false)]; + tensor var_1692_transpose_y_0 = const()[name = tensor("op_1692_transpose_y_0"), val = tensor(false)]; + tensor transpose_24_perm_0 = const()[name = tensor("transpose_24_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_25_perm_0 = const()[name = tensor("transpose_25_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_25 = transpose(perm = transpose_25_perm_0, x = keys_21)[name = tensor("transpose_39")]; + tensor transpose_24 = transpose(perm = transpose_24_perm_0, x = q_21)[name = tensor("transpose_40")]; + tensor var_1692 = matmul(transpose_x = var_1692_transpose_x_0, transpose_y = var_1692_transpose_y_0, x = transpose_24, y = transpose_25)[name = tensor("op_1692")]; + tensor attn_weights_19 = mul(x = var_1692, y = var_1686)[name = tensor("attn_weights_19")]; + tensor var_1694 = logical_not(x = attn_mask_15)[name = tensor("op_1694")]; + tensor var_1695 = const()[name = tensor("op_1695"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_21 = select(a = var_1695, b = attn_weights_19, cond = var_1694)[name = tensor("attn_weights_21")]; + tensor var_1697 = const()[name = tensor("op_1697"), val = tensor(-1)]; + tensor attn_weights_23 = softmax(axis = var_1697, x = attn_weights_21)[name = tensor("attn_weights_23")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor values_23 = transpose(perm = var_1643, x = values_21)[name = tensor("transpose_41")]; + tensor attn_output_7 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = attn_weights_23, y = values_23)[name = tensor("attn_output_7")]; + tensor var_1705 = const()[name = tensor("op_1705"), val = tensor([0, 2, 1, 3])]; + tensor var_1708 = const()[name = tensor("op_1708"), val = tensor([1, 1, 1024])]; + tensor var_1706 = transpose(perm = var_1705, x = attn_output_7)[name = tensor("transpose_38")]; + tensor input_35 = reshape(shape = var_1708, x = var_1706)[name = tensor("input_35")]; + tensor attn_out_7 = linear(bias = linear_0_bias_0, weight = attn3_out_proj_weight, x = input_35)[name = tensor("linear_14")]; + tensor var_1714 = const()[name = tensor("op_1714"), val = tensor(0x1p+0)]; + tensor var_1715 = add(x = position3, y = var_1714)[name = tensor("op_1715")]; + tensor input_37 = add(x = input_33, y = attn_out_7)[name = tensor("input_37")]; + tensor var_1719 = const()[name = tensor("op_1719"), val = tensor(0x1.4f8b58p-17)]; + tensor input_39_axes_0 = const()[name = tensor("input_39_axes_0"), val = tensor([-1])]; + tensor input_39 = layer_norm(axes = input_39_axes_0, beta = norm3_2_bias, epsilon = var_1719, gamma = norm3_2_weight, x = input_37)[name = tensor("input_39")]; + tensor var_1727 = linear(bias = linear_3_bias_0, weight = linear3_1_weight, x = input_39)[name = tensor("linear_15")]; + tensor input_41_mode_0 = const()[name = tensor("input_41_mode_0"), val = tensor("EXACT")]; + tensor input_41 = gelu(mode = input_41_mode_0, x = var_1727)[name = tensor("input_41")]; + tensor ffn_out_7 = linear(bias = linear_0_bias_0, weight = linear3_2_weight, x = input_41)[name = tensor("linear_16")]; + tensor input_43 = add(x = input_37, y = ffn_out_7)[name = tensor("input_43")]; + tensor var_1736 = const()[name = tensor("op_1736"), val = tensor(0x1.4f8b58p-17)]; + tensor x_9_axes_0 = const()[name = tensor("x_9_axes_0"), val = tensor([-1])]; + tensor x_9 = layer_norm(axes = x_9_axes_0, beta = norm4_1_bias, epsilon = var_1736, gamma = norm4_1_weight, x = input_43)[name = tensor("x_9")]; + tensor var_1768 = linear(bias = linear_1_bias_0, weight = attn4_in_proj_weight, x = x_9)[name = tensor("linear_17")]; + tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_9 = reshape(shape = var_1772, x = var_1768)[name = tensor("qkv_9")]; + tensor q_25_begin_0 = const()[name = tensor("q_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_25_end_0 = const()[name = tensor("q_25_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_25_end_mask_0 = const()[name = tensor("q_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_25_squeeze_mask_0 = const()[name = tensor("q_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_25 = slice_by_index(begin = q_25_begin_0, end = q_25_end_0, end_mask = q_25_end_mask_0, squeeze_mask = q_25_squeeze_mask_0, x = qkv_9)[name = tensor("q_25")]; + tensor k_17_begin_0 = const()[name = tensor("k_17_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_17_end_0 = const()[name = tensor("k_17_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_17_end_mask_0 = const()[name = tensor("k_17_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_17_squeeze_mask_0 = const()[name = tensor("k_17_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_17 = slice_by_index(begin = k_17_begin_0, end = k_17_end_0, end_mask = k_17_end_mask_0, squeeze_mask = k_17_squeeze_mask_0, x = qkv_9)[name = tensor("k_17")]; + tensor v_9_begin_0 = const()[name = tensor("v_9_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_9_end_0 = const()[name = tensor("v_9_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_9_end_mask_0 = const()[name = tensor("v_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_9_squeeze_mask_0 = const()[name = tensor("v_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_9 = slice_by_index(begin = v_9_begin_0, end = v_9_end_0, end_mask = v_9_end_mask_0, squeeze_mask = v_9_squeeze_mask_0, x = qkv_9)[name = tensor("v_9")]; + tensor freqs_9 = const()[name = tensor("freqs_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304368000)))]; + tensor var_1876 = const()[name = tensor("op_1876"), val = tensor([1, 1, 1, 1])]; + tensor ts_29 = reshape(shape = var_1876, x = position4)[name = tensor("ts_29")]; + tensor var_1880 = const()[name = tensor("op_1880"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_9 = reshape(shape = var_1880, x = q_25)[name = tensor("q_complex_9")]; + tensor var_1884 = const()[name = tensor("op_1884"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_9 = reshape(shape = var_1884, x = k_17)[name = tensor("k_complex_9")]; + tensor var_1888_begin_0 = const()[name = tensor("op_1888_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1888_end_0 = const()[name = tensor("op_1888_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1888_end_mask_0 = const()[name = tensor("op_1888_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1888_squeeze_mask_0 = const()[name = tensor("op_1888_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1888 = slice_by_index(begin = var_1888_begin_0, end = var_1888_end_0, end_mask = var_1888_end_mask_0, squeeze_mask = var_1888_squeeze_mask_0, x = q_complex_9)[name = tensor("op_1888")]; + tensor var_1896_begin_0 = const()[name = tensor("op_1896_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1896_end_0 = const()[name = tensor("op_1896_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1896_end_mask_0 = const()[name = tensor("op_1896_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1896_squeeze_mask_0 = const()[name = tensor("op_1896_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1896 = slice_by_index(begin = var_1896_begin_0, end = var_1896_end_0, end_mask = var_1896_end_mask_0, squeeze_mask = var_1896_squeeze_mask_0, x = q_complex_9)[name = tensor("op_1896")]; + tensor var_1904_begin_0 = const()[name = tensor("op_1904_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1904_end_0 = const()[name = tensor("op_1904_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1904_end_mask_0 = const()[name = tensor("op_1904_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1904_squeeze_mask_0 = const()[name = tensor("op_1904_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1904 = slice_by_index(begin = var_1904_begin_0, end = var_1904_end_0, end_mask = var_1904_end_mask_0, squeeze_mask = var_1904_squeeze_mask_0, x = k_complex_9)[name = tensor("op_1904")]; + tensor var_1912_begin_0 = const()[name = tensor("op_1912_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1912_end_0 = const()[name = tensor("op_1912_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1912_end_mask_0 = const()[name = tensor("op_1912_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1912_squeeze_mask_0 = const()[name = tensor("op_1912_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1912 = slice_by_index(begin = var_1912_begin_0, end = var_1912_end_0, end_mask = var_1912_end_mask_0, squeeze_mask = var_1912_squeeze_mask_0, x = k_complex_9)[name = tensor("op_1912")]; + tensor var_1918 = mul(x = freqs_9, y = ts_29)[name = tensor("op_1918")]; + tensor rotr_9 = cos(x = var_1918)[name = tensor("rotr_9")]; + tensor roti_9 = sin(x = var_1918)[name = tensor("roti_9")]; + tensor var_1922 = mul(x = var_1888, y = rotr_9)[name = tensor("op_1922")]; + tensor var_1923 = mul(x = var_1896, y = roti_9)[name = tensor("op_1923")]; + tensor qor_17 = sub(x = var_1922, y = var_1923)[name = tensor("qor_17")]; + tensor var_1926 = mul(x = var_1888, y = roti_9)[name = tensor("op_1926")]; + tensor var_1927 = mul(x = var_1896, y = rotr_9)[name = tensor("op_1927")]; + tensor qoi_17 = add(x = var_1926, y = var_1927)[name = tensor("qoi_17")]; + tensor var_1930 = mul(x = var_1904, y = rotr_9)[name = tensor("op_1930")]; + tensor var_1931 = mul(x = var_1912, y = roti_9)[name = tensor("op_1931")]; + tensor kor_17 = sub(x = var_1930, y = var_1931)[name = tensor("kor_17")]; + tensor var_1934 = mul(x = var_1904, y = roti_9)[name = tensor("op_1934")]; + tensor var_1935 = mul(x = var_1912, y = rotr_9)[name = tensor("op_1935")]; + tensor koi_17 = add(x = var_1934, y = var_1935)[name = tensor("koi_17")]; + tensor qo_9_axis_0 = const()[name = tensor("qo_9_axis_0"), val = tensor(-1)]; + tensor qo_9 = stack(axis = qo_9_axis_0, values = (qor_17, qoi_17))[name = tensor("qo_9")]; + tensor ko_9_axis_0 = const()[name = tensor("ko_9_axis_0"), val = tensor(-1)]; + tensor ko_9 = stack(axis = ko_9_axis_0, values = (kor_17, koi_17))[name = tensor("ko_9")]; + tensor var_1964 = const()[name = tensor("op_1964"), val = tensor([1, 1, 16, 64])]; + tensor q_27 = reshape(shape = var_1964, x = qo_9)[name = tensor("q_27")]; + tensor var_1966 = const()[name = tensor("op_1966"), val = tensor([1, 1, 16, 64])]; + tensor k_19 = reshape(shape = var_1966, x = ko_9)[name = tensor("k_19")]; + tensor _inversed_1988_y_0 = const()[name = tensor("_inversed_1988_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1988 = mul(x = ts_29, y = _inversed_1988_y_0)[name = tensor("_inversed_1988")]; + tensor var_1989 = floor(x = _inversed_1988)[name = tensor("op_1989")]; + tensor var_1990 = const()[name = tensor("op_1990"), val = tensor(0x1p+9)]; + tensor var_1991 = mul(x = var_1989, y = var_1990)[name = tensor("op_1991")]; + tensor write_indices_float_19 = sub(x = ts_29, y = var_1991)[name = tensor("write_indices_float_19")]; + tensor var_1998_dtype_0 = const()[name = tensor("op_1998_dtype_0"), val = tensor("int32")]; + tensor write_indices_9_reps_0 = const()[name = tensor("write_indices_9_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1998 = cast(dtype = var_1998_dtype_0, x = write_indices_float_19)[name = tensor("cast_109")]; + tensor write_indices_9 = tile(reps = write_indices_9_reps_0, x = var_1998)[name = tensor("write_indices_9")]; + tensor var_2006_begin_0 = const()[name = tensor("op_2006_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2006_end_0 = const()[name = tensor("op_2006_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2006_end_mask_0 = const()[name = tensor("op_2006_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2006_squeeze_mask_0 = const()[name = tensor("op_2006_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2006 = slice_by_index(begin = var_2006_begin_0, end = var_2006_end_0, end_mask = var_2006_end_mask_0, squeeze_mask = var_2006_squeeze_mask_0, x = cache4)[name = tensor("op_2006")]; + tensor var_2008_axis_0 = const()[name = tensor("op_2008_axis_0"), val = tensor(1)]; + tensor var_2008_mode_0 = const()[name = tensor("op_2008_mode_0"), val = tensor("update")]; + tensor var_2008_validate_indices_0 = const()[name = tensor("op_2008_validate_indices_0"), val = tensor(false)]; + tensor var_2008 = scatter_along_axis(axis = var_2008_axis_0, data = var_2006, indices = write_indices_9, mode = var_2008_mode_0, updates = k_19, validate_indices = var_2008_validate_indices_0)[name = tensor("op_2008")]; + tensor concat_30 = const()[name = tensor("concat_30"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_31 = const()[name = tensor("concat_31"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_20 = const()[name = tensor("shape_20"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_8 = const()[name = tensor("reduce_prod_8"), val = tensor(1048576)]; + tensor range_1d_8_start_0 = const()[name = tensor("range_1d_8_start_0"), val = tensor(0)]; + tensor range_1d_8_step_0 = const()[name = tensor("range_1d_8_step_0"), val = tensor(1)]; + tensor range_1d_8 = range_1d(end = reduce_prod_8, start = range_1d_8_start_0, step = range_1d_8_step_0)[name = tensor("range_1d_8")]; + tensor reshape_40 = reshape(shape = shape_20, x = range_1d_8)[name = tensor("reshape_40")]; + tensor slice_by_index_8 = slice_by_index(begin = concat_30, begin_mask = new_cache_9_internal_tensor_assign_1_begin_mask_0, end = concat_31, end_mask = new_cache_9_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_1_stride_0, x = reshape_40)[name = tensor("slice_by_index_8")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([-1])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = slice_by_index_8)[name = tensor("reshape_41")]; + tensor reshape_42_shape_0 = const()[name = tensor("reshape_42_shape_0"), val = tensor([-1])]; + tensor reshape_42 = reshape(shape = reshape_42_shape_0, x = var_2008)[name = tensor("reshape_42")]; + tensor reshape_43_shape_0 = const()[name = tensor("reshape_43_shape_0"), val = tensor([-1])]; + tensor reshape_43 = reshape(shape = reshape_43_shape_0, x = cache4)[name = tensor("reshape_43")]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8_validate_indices_0 = const()[name = tensor("scatter_8_validate_indices_0"), val = tensor(false)]; + tensor scatter_8 = scatter(axis = scatter_8_axis_0, data = reshape_43, indices = reshape_41, mode = scatter_8_mode_0, updates = reshape_42, validate_indices = scatter_8_validate_indices_0)[name = tensor("scatter_8")]; + tensor reshape_44 = reshape(shape = shape_20, x = scatter_8)[name = tensor("reshape_44")]; + tensor var_2016_begin_0 = const()[name = tensor("op_2016_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2016_end_0 = const()[name = tensor("op_2016_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2016_end_mask_0 = const()[name = tensor("op_2016_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2016_squeeze_mask_0 = const()[name = tensor("op_2016_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2016 = slice_by_index(begin = var_2016_begin_0, end = var_2016_end_0, end_mask = var_2016_end_mask_0, squeeze_mask = var_2016_squeeze_mask_0, x = reshape_44)[name = tensor("op_2016")]; + tensor var_2018_axis_0 = const()[name = tensor("op_2018_axis_0"), val = tensor(1)]; + tensor var_2018_mode_0 = const()[name = tensor("op_2018_mode_0"), val = tensor("update")]; + tensor var_2018_validate_indices_0 = const()[name = tensor("op_2018_validate_indices_0"), val = tensor(false)]; + tensor var_2018 = scatter_along_axis(axis = var_2018_axis_0, data = var_2016, indices = write_indices_9, mode = var_2018_mode_0, updates = v_9, validate_indices = var_2018_validate_indices_0)[name = tensor("op_2018")]; + tensor concat_32 = const()[name = tensor("concat_32"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_33 = const()[name = tensor("concat_33"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_21 = const()[name = tensor("shape_21"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_9 = const()[name = tensor("reduce_prod_9"), val = tensor(1048576)]; + tensor range_1d_9_start_0 = const()[name = tensor("range_1d_9_start_0"), val = tensor(0)]; + tensor range_1d_9_step_0 = const()[name = tensor("range_1d_9_step_0"), val = tensor(1)]; + tensor range_1d_9 = range_1d(end = reduce_prod_9, start = range_1d_9_start_0, step = range_1d_9_step_0)[name = tensor("range_1d_9")]; + tensor reshape_45 = reshape(shape = shape_21, x = range_1d_9)[name = tensor("reshape_45")]; + tensor slice_by_index_9 = slice_by_index(begin = concat_32, begin_mask = new_cache_9_internal_tensor_assign_2_begin_mask_0, end = concat_33, end_mask = new_cache_9_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_2_stride_0, x = reshape_45)[name = tensor("slice_by_index_9")]; + tensor reshape_46_shape_0 = const()[name = tensor("reshape_46_shape_0"), val = tensor([-1])]; + tensor reshape_46 = reshape(shape = reshape_46_shape_0, x = slice_by_index_9)[name = tensor("reshape_46")]; + tensor reshape_47_shape_0 = const()[name = tensor("reshape_47_shape_0"), val = tensor([-1])]; + tensor reshape_47 = reshape(shape = reshape_47_shape_0, x = var_2018)[name = tensor("reshape_47")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([-1])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = reshape_44)[name = tensor("reshape_48")]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9_validate_indices_0 = const()[name = tensor("scatter_9_validate_indices_0"), val = tensor(false)]; + tensor scatter_9 = scatter(axis = scatter_9_axis_0, data = reshape_48, indices = reshape_46, mode = scatter_9_mode_0, updates = reshape_47, validate_indices = scatter_9_validate_indices_0)[name = tensor("scatter_9")]; + tensor new_cache_9_internal_tensor_assign_2 = reshape(shape = shape_21, x = scatter_9)[name = tensor("reshape_49")]; + tensor keys_25_begin_0 = const()[name = tensor("keys_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_25_end_0 = const()[name = tensor("keys_25_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_25_end_mask_0 = const()[name = tensor("keys_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_25_squeeze_mask_0 = const()[name = tensor("keys_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_25 = slice_by_index(begin = keys_25_begin_0, end = keys_25_end_0, end_mask = keys_25_end_mask_0, squeeze_mask = keys_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("keys_25")]; + tensor values_25_begin_0 = const()[name = tensor("values_25_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_25_end_0 = const()[name = tensor("values_25_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_25_end_mask_0 = const()[name = tensor("values_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_25_squeeze_mask_0 = const()[name = tensor("values_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_25 = slice_by_index(begin = values_25_begin_0, end = values_25_end_0, end_mask = values_25_end_mask_0, squeeze_mask = values_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("values_25")]; + tensor var_2030 = not_equal(x = keys_25, y = keys_25)[name = tensor("op_2030")]; + tensor keys_27 = select(a = var_360, b = keys_25, cond = var_2030)[name = tensor("keys_27")]; + tensor var_2038 = not_equal(x = values_25, y = values_25)[name = tensor("op_2038")]; + tensor values_27 = select(a = var_360, b = values_25, cond = var_2038)[name = tensor("values_27")]; + tensor var_2062 = const()[name = tensor("op_2062"), val = tensor([0, 2, 1, 3])]; + tensor var_2075 = const()[name = tensor("op_2075"), val = tensor([1, 1, 1])]; + tensor var_2076 = reshape(shape = var_2075, x = position4)[name = tensor("op_2076")]; + tensor var_2093 = const()[name = tensor("op_2093"), val = tensor(0x1p+0)]; + tensor valid_len_9 = add(x = var_2076, y = var_2093)[name = tensor("valid_len_9")]; + tensor valid_mask_9 = less(x = k_positions_1_promoted, y = valid_len_9)[name = tensor("valid_mask_9")]; + tensor causal_mask_9 = less_equal(x = k_positions_1_promoted, y = var_2076)[name = tensor("causal_mask_9")]; + tensor attn_mask_17 = logical_and(x = valid_mask_9, y = causal_mask_9)[name = tensor("attn_mask_17")]; + tensor attn_mask_19_axes_0 = const()[name = tensor("attn_mask_19_axes_0"), val = tensor([1])]; + tensor attn_mask_19 = expand_dims(axes = attn_mask_19_axes_0, x = attn_mask_17)[name = tensor("attn_mask_19")]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2111_transpose_x_0 = const()[name = tensor("op_2111_transpose_x_0"), val = tensor(false)]; + tensor var_2111_transpose_y_0 = const()[name = tensor("op_2111_transpose_y_0"), val = tensor(false)]; + tensor transpose_26_perm_0 = const()[name = tensor("transpose_26_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_27_perm_0 = const()[name = tensor("transpose_27_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_27 = transpose(perm = transpose_27_perm_0, x = keys_27)[name = tensor("transpose_35")]; + tensor transpose_26 = transpose(perm = transpose_26_perm_0, x = q_27)[name = tensor("transpose_36")]; + tensor var_2111 = matmul(transpose_x = var_2111_transpose_x_0, transpose_y = var_2111_transpose_y_0, x = transpose_26, y = transpose_27)[name = tensor("op_2111")]; + tensor attn_weights_25 = mul(x = var_2111, y = var_2105)[name = tensor("attn_weights_25")]; + tensor var_2113 = logical_not(x = attn_mask_19)[name = tensor("op_2113")]; + tensor var_2114 = const()[name = tensor("op_2114"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_27 = select(a = var_2114, b = attn_weights_25, cond = var_2113)[name = tensor("attn_weights_27")]; + tensor var_2116 = const()[name = tensor("op_2116"), val = tensor(-1)]; + tensor attn_weights_29 = softmax(axis = var_2116, x = attn_weights_27)[name = tensor("attn_weights_29")]; + tensor attn_output_9_transpose_x_0 = const()[name = tensor("attn_output_9_transpose_x_0"), val = tensor(false)]; + tensor attn_output_9_transpose_y_0 = const()[name = tensor("attn_output_9_transpose_y_0"), val = tensor(false)]; + tensor values_29 = transpose(perm = var_2062, x = values_27)[name = tensor("transpose_37")]; + tensor attn_output_9 = matmul(transpose_x = attn_output_9_transpose_x_0, transpose_y = attn_output_9_transpose_y_0, x = attn_weights_29, y = values_29)[name = tensor("attn_output_9")]; + tensor var_2124 = const()[name = tensor("op_2124"), val = tensor([0, 2, 1, 3])]; + tensor var_2127 = const()[name = tensor("op_2127"), val = tensor([1, 1, 1024])]; + tensor var_2125 = transpose(perm = var_2124, x = attn_output_9)[name = tensor("transpose_34")]; + tensor input_45 = reshape(shape = var_2127, x = var_2125)[name = tensor("input_45")]; + tensor attn_out_9 = linear(bias = linear_0_bias_0, weight = attn4_out_proj_weight, x = input_45)[name = tensor("linear_18")]; + tensor var_2133 = const()[name = tensor("op_2133"), val = tensor(0x1p+0)]; + tensor var_2134 = add(x = position4, y = var_2133)[name = tensor("op_2134")]; + tensor input_47 = add(x = input_43, y = attn_out_9)[name = tensor("input_47")]; + tensor var_2138 = const()[name = tensor("op_2138"), val = tensor(0x1.4f8b58p-17)]; + tensor input_49_axes_0 = const()[name = tensor("input_49_axes_0"), val = tensor([-1])]; + tensor input_49 = layer_norm(axes = input_49_axes_0, beta = norm4_2_bias, epsilon = var_2138, gamma = norm4_2_weight, x = input_47)[name = tensor("input_49")]; + tensor var_2146 = linear(bias = linear_3_bias_0, weight = linear4_1_weight, x = input_49)[name = tensor("linear_19")]; + tensor input_51_mode_0 = const()[name = tensor("input_51_mode_0"), val = tensor("EXACT")]; + tensor input_51 = gelu(mode = input_51_mode_0, x = var_2146)[name = tensor("input_51")]; + tensor ffn_out_9 = linear(bias = linear_0_bias_0, weight = linear4_2_weight, x = input_51)[name = tensor("linear_20")]; + tensor input_53 = add(x = input_47, y = ffn_out_9)[name = tensor("input_53")]; + tensor var_2155 = const()[name = tensor("op_2155"), val = tensor(0x1.4f8b58p-17)]; + tensor x_axes_0 = const()[name = tensor("x_axes_0"), val = tensor([-1])]; + tensor x = layer_norm(axes = x_axes_0, beta = norm5_1_bias, epsilon = var_2155, gamma = norm5_1_weight, x = input_53)[name = tensor("x")]; + tensor var_2187 = linear(bias = linear_1_bias_0, weight = attn5_in_proj_weight, x = x)[name = tensor("linear_21")]; + tensor var_2191 = const()[name = tensor("op_2191"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv = reshape(shape = var_2191, x = var_2187)[name = tensor("qkv")]; + tensor q_31_begin_0 = const()[name = tensor("q_31_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_31_end_0 = const()[name = tensor("q_31_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_31_end_mask_0 = const()[name = tensor("q_31_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_31_squeeze_mask_0 = const()[name = tensor("q_31_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_31 = slice_by_index(begin = q_31_begin_0, end = q_31_end_0, end_mask = q_31_end_mask_0, squeeze_mask = q_31_squeeze_mask_0, x = qkv)[name = tensor("q_31")]; + tensor k_21_begin_0 = const()[name = tensor("k_21_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_21_end_0 = const()[name = tensor("k_21_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_21_end_mask_0 = const()[name = tensor("k_21_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_21_squeeze_mask_0 = const()[name = tensor("k_21_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_21 = slice_by_index(begin = k_21_begin_0, end = k_21_end_0, end_mask = k_21_end_mask_0, squeeze_mask = k_21_squeeze_mask_0, x = qkv)[name = tensor("k_21")]; + tensor v_begin_0 = const()[name = tensor("v_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_end_0 = const()[name = tensor("v_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_end_mask_0 = const()[name = tensor("v_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_squeeze_mask_0 = const()[name = tensor("v_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v = slice_by_index(begin = v_begin_0, end = v_end_0, end_mask = v_end_mask_0, squeeze_mask = v_squeeze_mask_0, x = qkv)[name = tensor("v")]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304368192)))]; + tensor var_2295 = const()[name = tensor("op_2295"), val = tensor([1, 1, 1, 1])]; + tensor ts = reshape(shape = var_2295, x = position5)[name = tensor("ts")]; + tensor var_2299 = const()[name = tensor("op_2299"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex = reshape(shape = var_2299, x = q_31)[name = tensor("q_complex")]; + tensor var_2303 = const()[name = tensor("op_2303"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex = reshape(shape = var_2303, x = k_21)[name = tensor("k_complex")]; + tensor var_2307_begin_0 = const()[name = tensor("op_2307_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2307_end_0 = const()[name = tensor("op_2307_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2307_end_mask_0 = const()[name = tensor("op_2307_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2307_squeeze_mask_0 = const()[name = tensor("op_2307_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2307 = slice_by_index(begin = var_2307_begin_0, end = var_2307_end_0, end_mask = var_2307_end_mask_0, squeeze_mask = var_2307_squeeze_mask_0, x = q_complex)[name = tensor("op_2307")]; + tensor var_2315_begin_0 = const()[name = tensor("op_2315_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2315_end_0 = const()[name = tensor("op_2315_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2315_end_mask_0 = const()[name = tensor("op_2315_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2315_squeeze_mask_0 = const()[name = tensor("op_2315_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2315 = slice_by_index(begin = var_2315_begin_0, end = var_2315_end_0, end_mask = var_2315_end_mask_0, squeeze_mask = var_2315_squeeze_mask_0, x = q_complex)[name = tensor("op_2315")]; + tensor var_2323_begin_0 = const()[name = tensor("op_2323_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2323_end_0 = const()[name = tensor("op_2323_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2323_end_mask_0 = const()[name = tensor("op_2323_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2323_squeeze_mask_0 = const()[name = tensor("op_2323_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2323 = slice_by_index(begin = var_2323_begin_0, end = var_2323_end_0, end_mask = var_2323_end_mask_0, squeeze_mask = var_2323_squeeze_mask_0, x = k_complex)[name = tensor("op_2323")]; + tensor var_2331_begin_0 = const()[name = tensor("op_2331_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2331_end_0 = const()[name = tensor("op_2331_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2331_end_mask_0 = const()[name = tensor("op_2331_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2331_squeeze_mask_0 = const()[name = tensor("op_2331_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2331 = slice_by_index(begin = var_2331_begin_0, end = var_2331_end_0, end_mask = var_2331_end_mask_0, squeeze_mask = var_2331_squeeze_mask_0, x = k_complex)[name = tensor("op_2331")]; + tensor var_2337 = mul(x = freqs, y = ts)[name = tensor("op_2337")]; + tensor rotr = cos(x = var_2337)[name = tensor("rotr")]; + tensor roti = sin(x = var_2337)[name = tensor("roti")]; + tensor var_2341 = mul(x = var_2307, y = rotr)[name = tensor("op_2341")]; + tensor var_2342 = mul(x = var_2315, y = roti)[name = tensor("op_2342")]; + tensor qor_21 = sub(x = var_2341, y = var_2342)[name = tensor("qor_21")]; + tensor var_2345 = mul(x = var_2307, y = roti)[name = tensor("op_2345")]; + tensor var_2346 = mul(x = var_2315, y = rotr)[name = tensor("op_2346")]; + tensor qoi_21 = add(x = var_2345, y = var_2346)[name = tensor("qoi_21")]; + tensor var_2349 = mul(x = var_2323, y = rotr)[name = tensor("op_2349")]; + tensor var_2350 = mul(x = var_2331, y = roti)[name = tensor("op_2350")]; + tensor kor_21 = sub(x = var_2349, y = var_2350)[name = tensor("kor_21")]; + tensor var_2353 = mul(x = var_2323, y = roti)[name = tensor("op_2353")]; + tensor var_2354 = mul(x = var_2331, y = rotr)[name = tensor("op_2354")]; + tensor koi_21 = add(x = var_2353, y = var_2354)[name = tensor("koi_21")]; + tensor qo_axis_0 = const()[name = tensor("qo_axis_0"), val = tensor(-1)]; + tensor qo = stack(axis = qo_axis_0, values = (qor_21, qoi_21))[name = tensor("qo")]; + tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; + tensor ko = stack(axis = ko_axis_0, values = (kor_21, koi_21))[name = tensor("ko")]; + tensor var_2383 = const()[name = tensor("op_2383"), val = tensor([1, 1, 16, 64])]; + tensor q_33 = reshape(shape = var_2383, x = qo)[name = tensor("q_33")]; + tensor var_2385 = const()[name = tensor("op_2385"), val = tensor([1, 1, 16, 64])]; + tensor k = reshape(shape = var_2385, x = ko)[name = tensor("k")]; + tensor _inversed_2407_y_0 = const()[name = tensor("_inversed_2407_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2407 = mul(x = ts, y = _inversed_2407_y_0)[name = tensor("_inversed_2407")]; + tensor var_2408 = floor(x = _inversed_2407)[name = tensor("op_2408")]; + tensor var_2409 = const()[name = tensor("op_2409"), val = tensor(0x1p+9)]; + tensor var_2410 = mul(x = var_2408, y = var_2409)[name = tensor("op_2410")]; + tensor write_indices_float = sub(x = ts, y = var_2410)[name = tensor("write_indices_float")]; + tensor var_2417_dtype_0 = const()[name = tensor("op_2417_dtype_0"), val = tensor("int32")]; + tensor write_indices_reps_0 = const()[name = tensor("write_indices_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2417 = cast(dtype = var_2417_dtype_0, x = write_indices_float)[name = tensor("cast_108")]; + tensor write_indices = tile(reps = write_indices_reps_0, x = var_2417)[name = tensor("write_indices")]; + tensor var_2425_begin_0 = const()[name = tensor("op_2425_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2425_end_0 = const()[name = tensor("op_2425_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2425_end_mask_0 = const()[name = tensor("op_2425_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2425_squeeze_mask_0 = const()[name = tensor("op_2425_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2425 = slice_by_index(begin = var_2425_begin_0, end = var_2425_end_0, end_mask = var_2425_end_mask_0, squeeze_mask = var_2425_squeeze_mask_0, x = cache5)[name = tensor("op_2425")]; + tensor var_2427_axis_0 = const()[name = tensor("op_2427_axis_0"), val = tensor(1)]; + tensor var_2427_mode_0 = const()[name = tensor("op_2427_mode_0"), val = tensor("update")]; + tensor var_2427_validate_indices_0 = const()[name = tensor("op_2427_validate_indices_0"), val = tensor(false)]; + tensor var_2427 = scatter_along_axis(axis = var_2427_axis_0, data = var_2425, indices = write_indices, mode = var_2427_mode_0, updates = k, validate_indices = var_2427_validate_indices_0)[name = tensor("op_2427")]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_38 = const()[name = tensor("concat_38"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_22 = const()[name = tensor("shape_22"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_10 = const()[name = tensor("reduce_prod_10"), val = tensor(1048576)]; + tensor range_1d_10_start_0 = const()[name = tensor("range_1d_10_start_0"), val = tensor(0)]; + tensor range_1d_10_step_0 = const()[name = tensor("range_1d_10_step_0"), val = tensor(1)]; + tensor range_1d_10 = range_1d(end = reduce_prod_10, start = range_1d_10_start_0, step = range_1d_10_step_0)[name = tensor("range_1d_10")]; + tensor reshape_50 = reshape(shape = shape_22, x = range_1d_10)[name = tensor("reshape_50")]; + tensor slice_by_index_10 = slice_by_index(begin = concat_37, begin_mask = new_cache_internal_tensor_assign_1_begin_mask_0, end = concat_38, end_mask = new_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_internal_tensor_assign_1_stride_0, x = reshape_50)[name = tensor("slice_by_index_10")]; + tensor reshape_51_shape_0 = const()[name = tensor("reshape_51_shape_0"), val = tensor([-1])]; + tensor reshape_51 = reshape(shape = reshape_51_shape_0, x = slice_by_index_10)[name = tensor("reshape_51")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([-1])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_2427)[name = tensor("reshape_52")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([-1])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = cache5)[name = tensor("reshape_53")]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10_validate_indices_0 = const()[name = tensor("scatter_10_validate_indices_0"), val = tensor(false)]; + tensor scatter_10 = scatter(axis = scatter_10_axis_0, data = reshape_53, indices = reshape_51, mode = scatter_10_mode_0, updates = reshape_52, validate_indices = scatter_10_validate_indices_0)[name = tensor("scatter_10")]; + tensor reshape_54 = reshape(shape = shape_22, x = scatter_10)[name = tensor("reshape_54")]; + tensor var_2435_begin_0 = const()[name = tensor("op_2435_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2435_end_0 = const()[name = tensor("op_2435_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2435_end_mask_0 = const()[name = tensor("op_2435_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2435_squeeze_mask_0 = const()[name = tensor("op_2435_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2435 = slice_by_index(begin = var_2435_begin_0, end = var_2435_end_0, end_mask = var_2435_end_mask_0, squeeze_mask = var_2435_squeeze_mask_0, x = reshape_54)[name = tensor("op_2435")]; + tensor var_2437_axis_0 = const()[name = tensor("op_2437_axis_0"), val = tensor(1)]; + tensor var_2437_mode_0 = const()[name = tensor("op_2437_mode_0"), val = tensor("update")]; + tensor var_2437_validate_indices_0 = const()[name = tensor("op_2437_validate_indices_0"), val = tensor(false)]; + tensor var_2437 = scatter_along_axis(axis = var_2437_axis_0, data = var_2435, indices = write_indices, mode = var_2437_mode_0, updates = v, validate_indices = var_2437_validate_indices_0)[name = tensor("op_2437")]; + tensor concat_39 = const()[name = tensor("concat_39"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_40 = const()[name = tensor("concat_40"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_23 = const()[name = tensor("shape_23"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_11 = const()[name = tensor("reduce_prod_11"), val = tensor(1048576)]; + tensor range_1d_11_start_0 = const()[name = tensor("range_1d_11_start_0"), val = tensor(0)]; + tensor range_1d_11_step_0 = const()[name = tensor("range_1d_11_step_0"), val = tensor(1)]; + tensor range_1d_11 = range_1d(end = reduce_prod_11, start = range_1d_11_start_0, step = range_1d_11_step_0)[name = tensor("range_1d_11")]; + tensor reshape_55 = reshape(shape = shape_23, x = range_1d_11)[name = tensor("reshape_55")]; + tensor slice_by_index_11 = slice_by_index(begin = concat_39, begin_mask = new_cache_internal_tensor_assign_2_begin_mask_0, end = concat_40, end_mask = new_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_internal_tensor_assign_2_stride_0, x = reshape_55)[name = tensor("slice_by_index_11")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([-1])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = slice_by_index_11)[name = tensor("reshape_56")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([-1])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = var_2437)[name = tensor("reshape_57")]; + tensor reshape_58_shape_0 = const()[name = tensor("reshape_58_shape_0"), val = tensor([-1])]; + tensor reshape_58 = reshape(shape = reshape_58_shape_0, x = reshape_54)[name = tensor("reshape_58")]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11_validate_indices_0 = const()[name = tensor("scatter_11_validate_indices_0"), val = tensor(false)]; + tensor scatter_11 = scatter(axis = scatter_11_axis_0, data = reshape_58, indices = reshape_56, mode = scatter_11_mode_0, updates = reshape_57, validate_indices = scatter_11_validate_indices_0)[name = tensor("scatter_11")]; + tensor new_cache_internal_tensor_assign_2 = reshape(shape = shape_23, x = scatter_11)[name = tensor("reshape_59")]; + tensor keys_31_begin_0 = const()[name = tensor("keys_31_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_31_end_0 = const()[name = tensor("keys_31_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_31_end_mask_0 = const()[name = tensor("keys_31_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_31_squeeze_mask_0 = const()[name = tensor("keys_31_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_31 = slice_by_index(begin = keys_31_begin_0, end = keys_31_end_0, end_mask = keys_31_end_mask_0, squeeze_mask = keys_31_squeeze_mask_0, x = new_cache_internal_tensor_assign_2)[name = tensor("keys_31")]; + tensor values_31_begin_0 = const()[name = tensor("values_31_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_31_end_0 = const()[name = tensor("values_31_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_31_end_mask_0 = const()[name = tensor("values_31_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_31_squeeze_mask_0 = const()[name = tensor("values_31_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_31 = slice_by_index(begin = values_31_begin_0, end = values_31_end_0, end_mask = values_31_end_mask_0, squeeze_mask = values_31_squeeze_mask_0, x = new_cache_internal_tensor_assign_2)[name = tensor("values_31")]; + tensor var_2449 = not_equal(x = keys_31, y = keys_31)[name = tensor("op_2449")]; + tensor keys_33 = select(a = var_360, b = keys_31, cond = var_2449)[name = tensor("keys_33")]; + tensor var_2457 = not_equal(x = values_31, y = values_31)[name = tensor("op_2457")]; + tensor values_33 = select(a = var_360, b = values_31, cond = var_2457)[name = tensor("values_33")]; + tensor var_2481 = const()[name = tensor("op_2481"), val = tensor([0, 2, 1, 3])]; + tensor var_2494 = const()[name = tensor("op_2494"), val = tensor([1, 1, 1])]; + tensor var_2495 = reshape(shape = var_2494, x = position5)[name = tensor("op_2495")]; + tensor var_2512 = const()[name = tensor("op_2512"), val = tensor(0x1p+0)]; + tensor valid_len = add(x = var_2495, y = var_2512)[name = tensor("valid_len")]; + tensor valid_mask = less(x = k_positions_1_promoted, y = valid_len)[name = tensor("valid_mask")]; + tensor causal_mask = less_equal(x = k_positions_1_promoted, y = var_2495)[name = tensor("causal_mask")]; + tensor attn_mask_21 = logical_and(x = valid_mask, y = causal_mask)[name = tensor("attn_mask_21")]; + tensor attn_mask_axes_0 = const()[name = tensor("attn_mask_axes_0"), val = tensor([1])]; + tensor attn_mask = expand_dims(axes = attn_mask_axes_0, x = attn_mask_21)[name = tensor("attn_mask")]; + tensor var_2524 = const()[name = tensor("op_2524"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2530_transpose_x_0 = const()[name = tensor("op_2530_transpose_x_0"), val = tensor(false)]; + tensor var_2530_transpose_y_0 = const()[name = tensor("op_2530_transpose_y_0"), val = tensor(false)]; + tensor transpose_28_perm_0 = const()[name = tensor("transpose_28_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_29_perm_0 = const()[name = tensor("transpose_29_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_29 = transpose(perm = transpose_29_perm_0, x = keys_33)[name = tensor("transpose_31")]; + tensor transpose_28 = transpose(perm = transpose_28_perm_0, x = q_33)[name = tensor("transpose_32")]; + tensor var_2530 = matmul(transpose_x = var_2530_transpose_x_0, transpose_y = var_2530_transpose_y_0, x = transpose_28, y = transpose_29)[name = tensor("op_2530")]; + tensor attn_weights_31 = mul(x = var_2530, y = var_2524)[name = tensor("attn_weights_31")]; + tensor var_2532 = logical_not(x = attn_mask)[name = tensor("op_2532")]; + tensor var_2533 = const()[name = tensor("op_2533"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_33 = select(a = var_2533, b = attn_weights_31, cond = var_2532)[name = tensor("attn_weights_33")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor(-1)]; + tensor attn_weights = softmax(axis = var_2535, x = attn_weights_33)[name = tensor("attn_weights")]; + tensor attn_output_transpose_x_0 = const()[name = tensor("attn_output_transpose_x_0"), val = tensor(false)]; + tensor attn_output_transpose_y_0 = const()[name = tensor("attn_output_transpose_y_0"), val = tensor(false)]; + tensor values = transpose(perm = var_2481, x = values_33)[name = tensor("transpose_33")]; + tensor attn_output = matmul(transpose_x = attn_output_transpose_x_0, transpose_y = attn_output_transpose_y_0, x = attn_weights, y = values)[name = tensor("attn_output")]; + tensor var_2543 = const()[name = tensor("op_2543"), val = tensor([0, 2, 1, 3])]; + tensor var_2546 = const()[name = tensor("op_2546"), val = tensor([1, 1, 1024])]; + tensor var_2544 = transpose(perm = var_2543, x = attn_output)[name = tensor("transpose_30")]; + tensor input_55 = reshape(shape = var_2546, x = var_2544)[name = tensor("input_55")]; + tensor attn_out = linear(bias = linear_0_bias_0, weight = attn5_out_proj_weight, x = input_55)[name = tensor("linear_22")]; + tensor var_2552 = const()[name = tensor("op_2552"), val = tensor(0x1p+0)]; + tensor var_2553 = add(x = position5, y = var_2552)[name = tensor("op_2553")]; + tensor input_57 = add(x = input_53, y = attn_out)[name = tensor("input_57")]; + tensor var_2557 = const()[name = tensor("op_2557"), val = tensor(0x1.4f8b58p-17)]; + tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; + tensor input_59 = layer_norm(axes = input_59_axes_0, beta = norm5_2_bias, epsilon = var_2557, gamma = norm5_2_weight, x = input_57)[name = tensor("input_59")]; + tensor var_2565 = linear(bias = linear_3_bias_0, weight = linear5_1_weight, x = input_59)[name = tensor("linear_23")]; + tensor input_61_mode_0 = const()[name = tensor("input_61_mode_0"), val = tensor("EXACT")]; + tensor input_61 = gelu(mode = input_61_mode_0, x = var_2565)[name = tensor("input_61")]; + tensor ffn_out = linear(bias = linear_0_bias_0, weight = linear5_2_weight, x = input_61)[name = tensor("linear_24")]; + tensor input_63 = add(x = input_57, y = ffn_out)[name = tensor("input_63")]; + tensor var_2574 = const()[name = tensor("op_2574"), val = tensor(0x1.4f8b58p-17)]; + tensor input_axes_0 = const()[name = tensor("input_axes_0"), val = tensor([-1])]; + tensor input = layer_norm(axes = input_axes_0, beta = out_norm_bias, epsilon = var_2574, gamma = out_norm_weight, x = input_63)[name = tensor("input")]; + tensor var_2582 = linear(bias = out_eos_bias, weight = out_eos_weight, x = input)[name = tensor("linear_25")]; + } -> (input, var_2582, new_cache_1_internal_tensor_assign_2, var_458, new_cache_3_internal_tensor_assign_2, var_877, new_cache_5_internal_tensor_assign_2, var_1296, new_cache_7_internal_tensor_assign_2, var_1715, new_cache_9_internal_tensor_assign_2, var_2134, new_cache_internal_tensor_assign_2, var_2553); +} \ No newline at end of file diff --git a/v2/english/flowlm_step.mlmodelc/weights/weight.bin b/v2/english/flowlm_step.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..26400ebdd675bfc02324acf423ee1d65742e9c26 --- /dev/null +++ b/v2/english/flowlm_step.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+ } +] \ No newline at end of file diff --git a/v2/english/mimi_decoder.mlmodelc/model.mil b/v2/english/mimi_decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..69ef039645ac6b4e76218d04f8327a3576d285bc --- /dev/null +++ b/v2/english/mimi_decoder.mlmodelc/model.mil @@ -0,0 +1,646 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor attn0_cache, tensor attn0_offset, tensor attn1_cache, tensor attn1_offset, tensor conv0_first, tensor conv0_prev, tensor conv_final_first, tensor conv_final_prev, tensor convtr0_partial, tensor convtr1_partial, tensor convtr2_partial, tensor latent, tensor res0_conv0_first, tensor res0_conv0_prev, tensor res0_conv1_first, tensor res0_conv1_prev, tensor res1_conv0_first, tensor res1_conv0_prev, tensor res1_conv1_first, tensor res1_conv1_prev, tensor res2_conv0_first, tensor res2_conv0_prev, tensor res2_conv1_first, tensor res2_conv1_prev, tensor upsample_partial) { + tensor emb_mean = const()[name = tensor("emb_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_std = const()[name = tensor("emb_std"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256)))]; + tensor mimi_quantizer_output_proj_weight = const()[name = tensor("mimi_quantizer_output_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor mimi_upsample_convtr_convtr_weight = const()[name = tensor("mimi_upsample_convtr_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66048)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm1_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131648)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133760)))]; + tensor mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135872)))]; + tensor mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3281664)))]; + tensor mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4330304)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm2_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4332416)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4334528)))]; + tensor mimi_decoder_transformer_transformer_layers_0_linear1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4336640)))]; + tensor mimi_decoder_transformer_transformer_layers_0_linear2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8531008)))]; + tensor mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12725376)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm1_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12727488)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12729600)))]; + tensor mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12731712)))]; + tensor mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15877504)))]; + tensor mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16926144)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm2_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16928256)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16930368)))]; + tensor mimi_decoder_transformer_transformer_layers_1_linear1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16932480)))]; + tensor mimi_decoder_transformer_transformer_layers_1_linear2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21126848)))]; + tensor mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25321216)))]; + tensor mimi_decoder_model_0_conv_bias = const()[name = tensor("mimi_decoder_model_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25323328)))]; + tensor mimi_decoder_model_0_conv_weight = const()[name = tensor("mimi_decoder_model_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25325440)))]; + tensor mimi_decoder_model_2_convtr_bias = const()[name = tensor("mimi_decoder_model_2_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32665536)))]; + tensor mimi_decoder_model_2_convtr_weight = const()[name = tensor("mimi_decoder_model_2_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32666624)))]; + tensor mimi_decoder_model_3_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_3_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958144)))]; + tensor mimi_decoder_model_3_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_3_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958720)))]; + tensor mimi_decoder_model_3_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_3_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39352000)))]; + tensor mimi_decoder_model_3_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_3_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39353088)))]; + tensor mimi_decoder_model_5_convtr_bias = const()[name = tensor("mimi_decoder_model_5_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484224)))]; + tensor mimi_decoder_model_5_convtr_weight = const()[name = tensor("mimi_decoder_model_5_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484800)))]; + tensor mimi_decoder_model_6_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_6_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40795584)))]; + tensor mimi_decoder_model_6_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_6_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40795904)))]; + tensor mimi_decoder_model_6_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_6_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40894272)))]; + tensor mimi_decoder_model_6_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_6_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40894848)))]; + tensor mimi_decoder_model_8_convtr_bias = const()[name = tensor("mimi_decoder_model_8_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40927680)))]; + tensor mimi_decoder_model_8_convtr_weight = const()[name = tensor("mimi_decoder_model_8_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40928000)))]; + tensor mimi_decoder_model_9_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_9_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41190208)))]; + tensor mimi_decoder_model_9_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_9_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41190400)))]; + tensor mimi_decoder_model_9_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_9_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41215040)))]; + tensor mimi_decoder_model_9_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_9_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41215360)))]; + tensor mimi_decoder_model_11_conv_bias = const()[name = tensor("mimi_decoder_model_11_conv_bias"), val = tensor([0x1.38p-15])]; + tensor mimi_decoder_model_11_conv_weight = const()[name = tensor("mimi_decoder_model_11_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41223616)))]; + tensor var_38 = mul(x = latent, y = emb_std)[name = tensor("op_38")]; + tensor denorm = add(x = var_38, y = emb_mean)[name = tensor("denorm")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = denorm)[name = tensor("input_1")]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("valid")]; + tensor x_1_strides_0 = const()[name = tensor("x_1_strides_0"), val = tensor([1])]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0])]; + tensor x_1_dilations_0 = const()[name = tensor("x_1_dilations_0"), val = tensor([1])]; + tensor x_1_groups_0 = const()[name = tensor("x_1_groups_0"), val = tensor(1)]; + tensor x_1 = conv(dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = mimi_quantizer_output_proj_weight, x = input_1)[name = tensor("x_1")]; + tensor var_62 = const()[name = tensor("op_62"), val = tensor(-1)]; + tensor y_1_pad_type_0 = const()[name = tensor("y_1_pad_type_0"), val = tensor("valid")]; + tensor y_1_strides_0 = const()[name = tensor("y_1_strides_0"), val = tensor([16])]; + tensor y_1_groups_0 = const()[name = tensor("y_1_groups_0"), val = tensor(512)]; + tensor y_1_pad_0 = const()[name = tensor("y_1_pad_0"), val = tensor([0, 0])]; + tensor y_1_dilations_0 = const()[name = tensor("y_1_dilations_0"), val = tensor([1])]; + tensor y_1_has_output_shape_output_shape_0 = const()[name = tensor("y_1_has_output_shape_output_shape_0"), val = tensor([1, 512, 32])]; + tensor y_1_has_output_shape = conv_transpose(dilations = y_1_dilations_0, groups = y_1_groups_0, output_shape = y_1_has_output_shape_output_shape_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = mimi_upsample_convtr_convtr_weight, x = x_1)[name = tensor("y_1_has_output_shape")]; + tensor var_72_begin_0 = const()[name = tensor("op_72_begin_0"), val = tensor([0, 0, 0])]; + tensor var_72_end_0 = const()[name = tensor("op_72_end_0"), val = tensor([1, 512, 16])]; + tensor var_72_end_mask_0 = const()[name = tensor("op_72_end_mask_0"), val = tensor([true, true, false])]; + tensor var_72 = slice_by_index(begin = var_72_begin_0, end = var_72_end_0, end_mask = var_72_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_72")]; + tensor var_73 = add(x = var_72, y = upsample_partial)[name = tensor("op_73")]; + tensor var_74_begin_0 = const()[name = tensor("op_74_begin_0"), val = tensor([0, 0, 16])]; + tensor var_74_end_0 = const()[name = tensor("op_74_end_0"), val = tensor([1, 512, 32])]; + tensor var_74_end_mask_0 = const()[name = tensor("op_74_end_mask_0"), val = tensor([true, true, true])]; + tensor var_74 = slice_by_index(begin = var_74_begin_0, end = var_74_end_0, end_mask = var_74_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_74")]; + tensor y_3_interleave_0 = const()[name = tensor("y_3_interleave_0"), val = tensor(false)]; + tensor y_3 = concat(axis = var_62, interleave = y_3_interleave_0, values = (var_73, var_74))[name = tensor("y_3")]; + tensor var_77_begin_0 = const()[name = tensor("op_77_begin_0"), val = tensor([0, 0, 16])]; + tensor var_77_end_0 = const()[name = tensor("op_77_end_0"), val = tensor([1, 512, 32])]; + tensor var_77_end_mask_0 = const()[name = tensor("op_77_end_mask_0"), val = tensor([true, true, true])]; + tensor var_77 = slice_by_index(begin = var_77_begin_0, end = var_77_end_0, end_mask = var_77_end_mask_0, x = y_3)[name = tensor("op_77")]; + tensor x_3_begin_0 = const()[name = tensor("x_3_begin_0"), val = tensor([0, 0, 0])]; + tensor x_3_end_0 = const()[name = tensor("x_3_end_0"), val = tensor([1, 512, 16])]; + tensor x_3_end_mask_0 = const()[name = tensor("x_3_end_mask_0"), val = tensor([true, true, false])]; + tensor x_3 = slice_by_index(begin = x_3_begin_0, end = x_3_end_0, end_mask = x_3_end_mask_0, x = y_3)[name = tensor("x_3")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(0)]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor(-1)]; + tensor var_100 = const()[name = tensor("op_100"), val = tensor(-0x1.ff933cp+127)]; + tensor var_102 = const()[name = tensor("op_102"), val = tensor(0x1.4f8b58p-17)]; + tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; + tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; + tensor input_3 = transpose(perm = input_3_perm_0, x = x_3)[name = tensor("transpose_19")]; + tensor query_1 = layer_norm(axes = query_1_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm1_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_0_norm1_weight, x = input_3)[name = tensor("query_1")]; + tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41224448)))]; + tensor projected_1 = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight, x = query_1)[name = tensor("linear_0")]; + tensor var_130 = const()[name = tensor("op_130"), val = tensor([1, 16, 3, 8, 64])]; + tensor packed_1 = reshape(shape = var_130, x = projected_1)[name = tensor("packed_1")]; + tensor var_132_split_sizes_0 = const()[name = tensor("op_132_split_sizes_0"), val = tensor([1, 1, 1])]; + tensor var_132_axis_0 = const()[name = tensor("op_132_axis_0"), val = tensor(2)]; + tensor var_132_0, tensor var_132_1, tensor var_132_2 = split(axis = var_132_axis_0, split_sizes = var_132_split_sizes_0, x = packed_1)[name = tensor("op_132")]; + tensor squeeze_0_axes_0 = const()[name = tensor("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = var_132_0)[name = tensor("squeeze_0")]; + tensor squeeze_1_axes_0 = const()[name = tensor("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = var_132_1)[name = tensor("squeeze_1")]; + tensor squeeze_2_axes_0 = const()[name = tensor("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = var_132_2)[name = tensor("squeeze_2")]; + tensor offset_3_begin_0 = const()[name = tensor("offset_3_begin_0"), val = tensor([0])]; + tensor offset_3_end_0 = const()[name = tensor("offset_3_end_0"), val = tensor([1])]; + tensor offset_3_end_mask_0 = const()[name = tensor("offset_3_end_mask_0"), val = tensor([false])]; + tensor offset_3_squeeze_mask_0 = const()[name = tensor("offset_3_squeeze_mask_0"), val = tensor([true])]; + tensor offset_3 = slice_by_index(begin = offset_3_begin_0, end = offset_3_end_0, end_mask = offset_3_end_mask_0, squeeze_mask = offset_3_squeeze_mask_0, x = attn0_offset)[name = tensor("offset_3")]; + tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230656)))]; + tensor ts_1_promoted = const()[name = tensor("ts_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230848)))]; + tensor ts_3 = add(x = ts_1_promoted, y = offset_3)[name = tensor("ts_3")]; + tensor var_148 = const()[name = tensor("op_148"), val = tensor([-1, 1, 1])]; + tensor ts_5 = reshape(shape = var_148, x = ts_3)[name = tensor("ts_5")]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 16, 8, 32, 2])]; + tensor q_3 = reshape(shape = var_150, x = squeeze_0)[name = tensor("q_3")]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 16, 8, 32, 2])]; + tensor k_3 = reshape(shape = var_152, x = squeeze_1)[name = tensor("k_3")]; + tensor var_154_begin_0 = const()[name = tensor("op_154_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_154_end_0 = const()[name = tensor("op_154_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_154_end_mask_0 = const()[name = tensor("op_154_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_154_squeeze_mask_0 = const()[name = tensor("op_154_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_154 = slice_by_index(begin = var_154_begin_0, end = var_154_end_0, end_mask = var_154_end_mask_0, squeeze_mask = var_154_squeeze_mask_0, x = q_3)[name = tensor("op_154")]; + tensor var_156_begin_0 = const()[name = tensor("op_156_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_156_end_0 = const()[name = tensor("op_156_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_156_end_mask_0 = const()[name = tensor("op_156_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_156_squeeze_mask_0 = const()[name = tensor("op_156_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_156 = slice_by_index(begin = var_156_begin_0, end = var_156_end_0, end_mask = var_156_end_mask_0, squeeze_mask = var_156_squeeze_mask_0, x = q_3)[name = tensor("op_156")]; + tensor var_158_begin_0 = const()[name = tensor("op_158_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_158_end_0 = const()[name = tensor("op_158_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_158_end_mask_0 = const()[name = tensor("op_158_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_158_squeeze_mask_0 = const()[name = tensor("op_158_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_158 = slice_by_index(begin = var_158_begin_0, end = var_158_end_0, end_mask = var_158_end_mask_0, squeeze_mask = var_158_squeeze_mask_0, x = k_3)[name = tensor("op_158")]; + tensor var_160_begin_0 = const()[name = tensor("op_160_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_160_end_0 = const()[name = tensor("op_160_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_160_end_mask_0 = const()[name = tensor("op_160_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_160_squeeze_mask_0 = const()[name = tensor("op_160_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_160 = slice_by_index(begin = var_160_begin_0, end = var_160_end_0, end_mask = var_160_end_mask_0, squeeze_mask = var_160_squeeze_mask_0, x = k_3)[name = tensor("op_160")]; + tensor var_162 = mul(x = freqs_1, y = ts_5)[name = tensor("op_162")]; + tensor rotr_1 = cos(x = var_162)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_162)[name = tensor("roti_1")]; + tensor var_166 = mul(x = var_154, y = rotr_1)[name = tensor("op_166")]; + tensor var_167 = mul(x = var_156, y = roti_1)[name = tensor("op_167")]; + tensor qor_1 = sub(x = var_166, y = var_167)[name = tensor("qor_1")]; + tensor var_169 = mul(x = var_154, y = roti_1)[name = tensor("op_169")]; + tensor var_170 = mul(x = var_156, y = rotr_1)[name = tensor("op_170")]; + tensor qoi_1 = add(x = var_169, y = var_170)[name = tensor("qoi_1")]; + tensor var_172 = mul(x = var_158, y = rotr_1)[name = tensor("op_172")]; + tensor var_173 = mul(x = var_160, y = roti_1)[name = tensor("op_173")]; + tensor kor_1 = sub(x = var_172, y = var_173)[name = tensor("kor_1")]; + tensor var_175 = mul(x = var_158, y = roti_1)[name = tensor("op_175")]; + tensor var_176 = mul(x = var_160, y = rotr_1)[name = tensor("op_176")]; + tensor koi_1 = add(x = var_175, y = var_176)[name = tensor("koi_1")]; + tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; + tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; + tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; + tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; + tensor var_186 = const()[name = tensor("op_186"), val = tensor([1, 16, 8, 64])]; + tensor q_5 = reshape(shape = var_186, x = qo_1)[name = tensor("q_5")]; + tensor var_188 = const()[name = tensor("op_188"), val = tensor([1, 16, 8, 64])]; + tensor k_5 = reshape(shape = var_188, x = ko_1)[name = tensor("k_5")]; + tensor capacity_1 = const()[name = tensor("capacity_1"), val = tensor([256])]; + tensor var_193_dtype_0 = const()[name = tensor("op_193_dtype_0"), val = tensor("int32")]; + tensor var_194 = const()[name = tensor("op_194"), val = tensor([1, 1])]; + tensor var_193 = cast(dtype = var_193_dtype_0, x = attn0_offset)[name = tensor("cast_49")]; + tensor write_base_1 = reshape(shape = var_194, x = var_193)[name = tensor("write_base_1")]; + tensor write_range_1 = const()[name = tensor("write_range_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; + tensor abs_idx_1 = add(x = write_base_1, y = write_range_1)[name = tensor("abs_idx_1")]; + tensor wrapped_1_div = floor_div(x = abs_idx_1, y = capacity_1)[name = tensor("wrapped_1_div")]; + tensor wrapped_1_div_scaled = mul(x = wrapped_1_div, y = capacity_1)[name = tensor("wrapped_1_div_scaled")]; + tensor wrapped_1 = sub(x = abs_idx_1, y = wrapped_1_div_scaled)[name = tensor("wrapped_1")]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 16, 1, 1])]; + tensor var_202 = reshape(shape = var_201, x = wrapped_1)[name = tensor("op_202")]; + tensor write_indexes_1_reps_0 = const()[name = tensor("write_indexes_1_reps_0"), val = tensor([1, 1, 8, 64])]; + tensor write_indexes_1 = tile(reps = write_indexes_1_reps_0, x = var_202)[name = tensor("write_indexes_1")]; + tensor var_205_begin_0 = const()[name = tensor("op_205_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_205_end_0 = const()[name = tensor("op_205_end_0"), val = tensor([1, 1, 256, 8, 64])]; + tensor var_205_end_mask_0 = const()[name = tensor("op_205_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_205_squeeze_mask_0 = const()[name = tensor("op_205_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_205 = slice_by_index(begin = var_205_begin_0, end = var_205_end_0, end_mask = var_205_end_mask_0, squeeze_mask = var_205_squeeze_mask_0, x = attn0_cache)[name = tensor("op_205")]; + tensor new_k_cache_1_axis_0 = const()[name = tensor("new_k_cache_1_axis_0"), val = tensor(1)]; + tensor new_k_cache_1_mode_0 = const()[name = tensor("new_k_cache_1_mode_0"), val = tensor("update")]; + tensor new_k_cache_1_validate_indices_0 = const()[name = tensor("new_k_cache_1_validate_indices_0"), val = tensor(false)]; + tensor new_k_cache_1 = scatter_along_axis(axis = new_k_cache_1_axis_0, data = var_205, indices = write_indexes_1, mode = new_k_cache_1_mode_0, updates = k_5, validate_indices = new_k_cache_1_validate_indices_0)[name = tensor("new_k_cache_1")]; + tensor var_207_begin_0 = const()[name = tensor("op_207_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_207_end_0 = const()[name = tensor("op_207_end_0"), val = tensor([2, 1, 256, 8, 64])]; + tensor var_207_end_mask_0 = const()[name = tensor("op_207_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_207_squeeze_mask_0 = const()[name = tensor("op_207_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_207 = slice_by_index(begin = var_207_begin_0, end = var_207_end_0, end_mask = var_207_end_mask_0, squeeze_mask = var_207_squeeze_mask_0, x = attn0_cache)[name = tensor("op_207")]; + tensor new_v_cache_1_axis_0 = const()[name = tensor("new_v_cache_1_axis_0"), val = tensor(1)]; + tensor new_v_cache_1_mode_0 = const()[name = tensor("new_v_cache_1_mode_0"), val = tensor("update")]; + tensor new_v_cache_1_validate_indices_0 = const()[name = tensor("new_v_cache_1_validate_indices_0"), val = tensor(false)]; + tensor new_v_cache_1 = scatter_along_axis(axis = new_v_cache_1_axis_0, data = var_207, indices = write_indexes_1, mode = new_v_cache_1_mode_0, updates = squeeze_2, validate_indices = new_v_cache_1_validate_indices_0)[name = tensor("new_v_cache_1")]; + tensor var_210_axis_0 = const()[name = tensor("op_210_axis_0"), val = tensor(0)]; + tensor var_210 = stack(axis = var_210_axis_0, values = (new_k_cache_1, new_v_cache_1))[name = tensor("op_210")]; + tensor var_211 = not_equal(x = new_k_cache_1, y = new_k_cache_1)[name = tensor("op_211")]; + tensor var_212 = const()[name = tensor("op_212"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230976)))]; + tensor new_k_cache_3 = select(a = var_212, b = new_k_cache_1, cond = var_211)[name = tensor("new_k_cache_3")]; + tensor var_214 = not_equal(x = new_v_cache_1, y = new_v_cache_1)[name = tensor("op_214")]; + tensor new_v_cache_3 = select(a = var_212, b = new_v_cache_1, cond = var_214)[name = tensor("new_v_cache_3")]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([0, 2, 1, 3])]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; + tensor var_222 = reshape(shape = var_221, x = attn0_offset)[name = tensor("op_222")]; + tensor var_224_promoted = const()[name = tensor("op_224_promoted"), val = tensor([0x1.ep+3])]; + tensor var_225 = add(x = var_222, y = var_224_promoted)[name = tensor("op_225")]; + tensor last_pos_1_dtype_0 = const()[name = tensor("last_pos_1_dtype_0"), val = tensor("int32")]; + tensor slot_idx_1 = const()[name = tensor("slot_idx_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255]])]; + tensor last_pos_1 = cast(dtype = last_pos_1_dtype_0, x = var_225)[name = tensor("cast_48")]; + tensor diff_1 = sub(x = last_pos_1, y = slot_idx_1)[name = tensor("diff_1")]; + tensor var_231_div = floor_div(x = diff_1, y = capacity_1)[name = tensor("op_231_div")]; + tensor var_231_div_scaled = mul(x = var_231_div, y = capacity_1)[name = tensor("op_231_div_scaled")]; + tensor var_231 = sub(x = diff_1, y = var_231_div_scaled)[name = tensor("op_231")]; + tensor pos_k_1 = sub(x = last_pos_1, y = var_231)[name = tensor("pos_k_1")]; + tensor var_237_promoted = const()[name = tensor("op_237_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41755328)))]; + tensor pos_q_1 = add(x = var_222, y = var_237_promoted)[name = tensor("pos_q_1")]; + tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([2])]; + tensor var_241 = expand_dims(axes = var_241_axes_0, x = pos_q_1)[name = tensor("op_241")]; + tensor var_243_axes_0 = const()[name = tensor("op_243_axes_0"), val = tensor([1])]; + tensor var_243 = expand_dims(axes = var_243_axes_0, x = pos_k_1)[name = tensor("op_243")]; + tensor var_244_promoted_dtype_0 = const()[name = tensor("op_244_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_244_promoted = cast(dtype = var_244_promoted_dtype_0, x = var_243)[name = tensor("cast_47")]; + tensor delta_1 = sub(x = var_241, y = var_244_promoted)[name = tensor("delta_1")]; + tensor valid_1 = greater_equal(x = var_243, y = var_86)[name = tensor("valid_1")]; + tensor var_253 = const()[name = tensor("op_253"), val = tensor([1, 1, 1])]; + tensor var_254 = reshape(shape = var_253, x = attn0_offset)[name = tensor("op_254")]; + tensor var_256_promoted = const()[name = tensor("op_256_promoted"), val = tensor([0x1.ep+3])]; + tensor var_257 = add(x = var_254, y = var_256_promoted)[name = tensor("op_257")]; + tensor var_258 = less_equal(x = var_244_promoted, y = var_257)[name = tensor("op_258")]; + tensor valid_3 = logical_and(x = valid_1, y = var_258)[name = tensor("valid_3")]; + tensor var_86_promoted = const()[name = tensor("op_86_promoted"), val = tensor(0x0p+0)]; + tensor var_260 = greater_equal(x = delta_1, y = var_86_promoted)[name = tensor("op_260")]; + tensor attn_mask_1 = logical_and(x = valid_3, y = var_260)[name = tensor("attn_mask_1")]; + tensor var_98_promoted = const()[name = tensor("op_98_promoted"), val = tensor(0x1.f4p+7)]; + tensor var_262 = less(x = delta_1, y = var_98_promoted)[name = tensor("op_262")]; + tensor attn_mask_3 = logical_and(x = attn_mask_1, y = var_262)[name = tensor("attn_mask_3")]; + tensor attn_mask_5_axes_0 = const()[name = tensor("attn_mask_5_axes_0"), val = tensor([1])]; + tensor attn_mask_5 = expand_dims(axes = attn_mask_5_axes_0, x = attn_mask_3)[name = tensor("attn_mask_5")]; + tensor var_267_transpose_x_0 = const()[name = tensor("op_267_transpose_x_0"), val = tensor(false)]; + tensor var_267_transpose_y_0 = const()[name = tensor("op_267_transpose_y_0"), val = tensor(false)]; + tensor transpose_6_perm_0 = const()[name = tensor("transpose_6_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_7_perm_0 = const()[name = tensor("transpose_7_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_7 = transpose(perm = transpose_7_perm_0, x = new_k_cache_3)[name = tensor("transpose_16")]; + tensor transpose_6 = transpose(perm = transpose_6_perm_0, x = q_5)[name = tensor("transpose_17")]; + tensor var_267 = matmul(transpose_x = var_267_transpose_x_0, transpose_y = var_267_transpose_y_0, x = transpose_6, y = transpose_7)[name = tensor("op_267")]; + tensor var_268 = const()[name = tensor("op_268"), val = tensor(0x1p-3)]; + tensor attn_1 = mul(x = var_267, y = var_268)[name = tensor("attn_1")]; + tensor var_270 = logical_not(x = attn_mask_5)[name = tensor("op_270")]; + tensor attn_3 = select(a = var_100, b = attn_1, cond = var_270)[name = tensor("attn_3")]; + tensor attn_5 = softmax(axis = var_91, x = attn_3)[name = tensor("attn_5")]; + tensor x_5_transpose_x_0 = const()[name = tensor("x_5_transpose_x_0"), val = tensor(false)]; + tensor x_5_transpose_y_0 = const()[name = tensor("x_5_transpose_y_0"), val = tensor(false)]; + tensor v_attn_1 = transpose(perm = var_219, x = new_v_cache_3)[name = tensor("transpose_18")]; + tensor x_5 = matmul(transpose_x = x_5_transpose_x_0, transpose_y = x_5_transpose_y_0, x = attn_5, y = v_attn_1)[name = tensor("x_5")]; + tensor var_274_perm_0 = const()[name = tensor("op_274_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_275 = const()[name = tensor("op_275"), val = tensor([1, 16, 512])]; + tensor var_274 = transpose(perm = var_274_perm_0, x = x_5)[name = tensor("transpose_15")]; + tensor input_5 = reshape(shape = var_275, x = var_274)[name = tensor("input_5")]; + tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41755456)))]; + tensor x_7 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight, x = input_5)[name = tensor("linear_1")]; + tensor var_284 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale, y = x_7)[name = tensor("op_284")]; + tensor input_7 = add(x = input_3, y = var_284)[name = tensor("input_7")]; + tensor input_9_axes_0 = const()[name = tensor("input_9_axes_0"), val = tensor([-1])]; + tensor input_9 = layer_norm(axes = input_9_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm2_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_0_norm2_weight, x = input_7)[name = tensor("input_9")]; + tensor linear_2_bias_0 = const()[name = tensor("linear_2_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41757568)))]; + tensor var_291 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear1_weight, x = input_9)[name = tensor("linear_2")]; + tensor input_11_mode_0 = const()[name = tensor("input_11_mode_0"), val = tensor("EXACT")]; + tensor input_11 = gelu(mode = input_11_mode_0, x = var_291)[name = tensor("input_11")]; + tensor x_9 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear2_weight, x = input_11)[name = tensor("linear_3")]; + tensor var_297 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale, y = x_9)[name = tensor("op_297")]; + tensor input_13 = add(x = input_7, y = var_297)[name = tensor("input_13")]; + tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; + tensor query = layer_norm(axes = query_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm1_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_1_norm1_weight, x = input_13)[name = tensor("query")]; + tensor projected = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight, x = query)[name = tensor("linear_4")]; + tensor var_320 = const()[name = tensor("op_320"), val = tensor([1, 16, 3, 8, 64])]; + tensor packed = reshape(shape = var_320, x = projected)[name = tensor("packed")]; + tensor var_322_split_sizes_0 = const()[name = tensor("op_322_split_sizes_0"), val = tensor([1, 1, 1])]; + tensor var_322_axis_0 = const()[name = tensor("op_322_axis_0"), val = tensor(2)]; + tensor var_322_0, tensor var_322_1, tensor var_322_2 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = packed)[name = tensor("op_322")]; + tensor squeeze_3_axes_0 = const()[name = tensor("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = var_322_0)[name = tensor("squeeze_3")]; + tensor squeeze_4_axes_0 = const()[name = tensor("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4 = squeeze(axes = squeeze_4_axes_0, x = var_322_1)[name = tensor("squeeze_4")]; + tensor squeeze_5_axes_0 = const()[name = tensor("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5 = squeeze(axes = squeeze_5_axes_0, x = var_322_2)[name = tensor("squeeze_5")]; + tensor offset_begin_0 = const()[name = tensor("offset_begin_0"), val = tensor([0])]; + tensor offset_end_0 = const()[name = tensor("offset_end_0"), val = tensor([1])]; + tensor offset_end_mask_0 = const()[name = tensor("offset_end_mask_0"), val = tensor([false])]; + tensor offset_squeeze_mask_0 = const()[name = tensor("offset_squeeze_mask_0"), val = tensor([true])]; + tensor offset = slice_by_index(begin = offset_begin_0, end = offset_end_0, end_mask = offset_end_mask_0, squeeze_mask = offset_squeeze_mask_0, x = attn1_offset)[name = tensor("offset")]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41765824)))]; + tensor ts_7_promoted = const()[name = tensor("ts_7_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766016)))]; + tensor ts_9 = add(x = ts_7_promoted, y = offset)[name = tensor("ts_9")]; + tensor var_338 = const()[name = tensor("op_338"), val = tensor([-1, 1, 1])]; + tensor ts = reshape(shape = var_338, x = ts_9)[name = tensor("ts")]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 16, 8, 32, 2])]; + tensor q_9 = reshape(shape = var_340, x = squeeze_3)[name = tensor("q_9")]; + tensor var_342 = const()[name = tensor("op_342"), val = tensor([1, 16, 8, 32, 2])]; + tensor k_9 = reshape(shape = var_342, x = squeeze_4)[name = tensor("k_9")]; + tensor var_344_begin_0 = const()[name = tensor("op_344_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_344_end_0 = const()[name = tensor("op_344_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_344_end_mask_0 = const()[name = tensor("op_344_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_344_squeeze_mask_0 = const()[name = tensor("op_344_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_344 = slice_by_index(begin = var_344_begin_0, end = var_344_end_0, end_mask = var_344_end_mask_0, squeeze_mask = var_344_squeeze_mask_0, x = q_9)[name = tensor("op_344")]; + tensor var_346_begin_0 = const()[name = tensor("op_346_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_346_end_0 = const()[name = tensor("op_346_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_346_end_mask_0 = const()[name = tensor("op_346_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_346_squeeze_mask_0 = const()[name = tensor("op_346_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_346 = slice_by_index(begin = var_346_begin_0, end = var_346_end_0, end_mask = var_346_end_mask_0, squeeze_mask = var_346_squeeze_mask_0, x = q_9)[name = tensor("op_346")]; + tensor var_348_begin_0 = const()[name = tensor("op_348_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_348_end_0 = const()[name = tensor("op_348_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_348_end_mask_0 = const()[name = tensor("op_348_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_348_squeeze_mask_0 = const()[name = tensor("op_348_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_348 = slice_by_index(begin = var_348_begin_0, end = var_348_end_0, end_mask = var_348_end_mask_0, squeeze_mask = var_348_squeeze_mask_0, x = k_9)[name = tensor("op_348")]; + tensor var_350_begin_0 = const()[name = tensor("op_350_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_350_end_0 = const()[name = tensor("op_350_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_350_end_mask_0 = const()[name = tensor("op_350_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_350_squeeze_mask_0 = const()[name = tensor("op_350_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_350 = slice_by_index(begin = var_350_begin_0, end = var_350_end_0, end_mask = var_350_end_mask_0, squeeze_mask = var_350_squeeze_mask_0, x = k_9)[name = tensor("op_350")]; + tensor var_352 = mul(x = freqs, y = ts)[name = tensor("op_352")]; + tensor rotr = cos(x = var_352)[name = tensor("rotr")]; + tensor roti = sin(x = var_352)[name = tensor("roti")]; + tensor var_356 = mul(x = var_344, y = rotr)[name = tensor("op_356")]; + tensor var_357 = mul(x = var_346, y = roti)[name = tensor("op_357")]; + tensor qor_5 = sub(x = var_356, y = var_357)[name = tensor("qor_5")]; + tensor var_359 = mul(x = var_344, y = roti)[name = tensor("op_359")]; + tensor var_360 = mul(x = var_346, y = rotr)[name = tensor("op_360")]; + tensor qoi_5 = add(x = var_359, y = var_360)[name = tensor("qoi_5")]; + tensor var_362 = mul(x = var_348, y = rotr)[name = tensor("op_362")]; + tensor var_363 = mul(x = var_350, y = roti)[name = tensor("op_363")]; + tensor kor_5 = sub(x = var_362, y = var_363)[name = tensor("kor_5")]; + tensor var_365 = mul(x = var_348, y = roti)[name = tensor("op_365")]; + tensor var_366 = mul(x = var_350, y = rotr)[name = tensor("op_366")]; + tensor koi_5 = add(x = var_365, y = var_366)[name = tensor("koi_5")]; + tensor qo_axis_0 = const()[name = tensor("qo_axis_0"), val = tensor(-1)]; + tensor qo = stack(axis = qo_axis_0, values = (qor_5, qoi_5))[name = tensor("qo")]; + tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; + tensor ko = stack(axis = ko_axis_0, values = (kor_5, koi_5))[name = tensor("ko")]; + tensor var_376 = const()[name = tensor("op_376"), val = tensor([1, 16, 8, 64])]; + tensor q = reshape(shape = var_376, x = qo)[name = tensor("q")]; + tensor var_378 = const()[name = tensor("op_378"), val = tensor([1, 16, 8, 64])]; + tensor k = reshape(shape = var_378, x = ko)[name = tensor("k")]; + tensor capacity = const()[name = tensor("capacity"), val = tensor([256])]; + tensor var_383_dtype_0 = const()[name = tensor("op_383_dtype_0"), val = tensor("int32")]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; + tensor var_383 = cast(dtype = var_383_dtype_0, x = attn1_offset)[name = tensor("cast_46")]; + tensor write_base = reshape(shape = var_384, x = var_383)[name = tensor("write_base")]; + tensor write_range = const()[name = tensor("write_range"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; + tensor abs_idx = add(x = write_base, y = write_range)[name = tensor("abs_idx")]; + tensor wrapped_div = floor_div(x = abs_idx, y = capacity)[name = tensor("wrapped_div")]; + tensor wrapped_div_scaled = mul(x = wrapped_div, y = capacity)[name = tensor("wrapped_div_scaled")]; + tensor wrapped = sub(x = abs_idx, y = wrapped_div_scaled)[name = tensor("wrapped")]; + tensor var_391 = const()[name = tensor("op_391"), val = tensor([1, 16, 1, 1])]; + tensor var_392 = reshape(shape = var_391, x = wrapped)[name = tensor("op_392")]; + tensor write_indexes_reps_0 = const()[name = tensor("write_indexes_reps_0"), val = tensor([1, 1, 8, 64])]; + tensor write_indexes = tile(reps = write_indexes_reps_0, x = var_392)[name = tensor("write_indexes")]; + tensor var_395_begin_0 = const()[name = tensor("op_395_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_395_end_0 = const()[name = tensor("op_395_end_0"), val = tensor([1, 1, 256, 8, 64])]; + tensor var_395_end_mask_0 = const()[name = tensor("op_395_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_395_squeeze_mask_0 = const()[name = tensor("op_395_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_395 = slice_by_index(begin = var_395_begin_0, end = var_395_end_0, end_mask = var_395_end_mask_0, squeeze_mask = var_395_squeeze_mask_0, x = attn1_cache)[name = tensor("op_395")]; + tensor new_k_cache_5_axis_0 = const()[name = tensor("new_k_cache_5_axis_0"), val = tensor(1)]; + tensor new_k_cache_5_mode_0 = const()[name = tensor("new_k_cache_5_mode_0"), val = tensor("update")]; + tensor new_k_cache_5_validate_indices_0 = const()[name = tensor("new_k_cache_5_validate_indices_0"), val = tensor(false)]; + tensor new_k_cache_5 = scatter_along_axis(axis = new_k_cache_5_axis_0, data = var_395, indices = write_indexes, mode = new_k_cache_5_mode_0, updates = k, validate_indices = new_k_cache_5_validate_indices_0)[name = tensor("new_k_cache_5")]; + tensor var_397_begin_0 = const()[name = tensor("op_397_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_397_end_0 = const()[name = tensor("op_397_end_0"), val = tensor([2, 1, 256, 8, 64])]; + tensor var_397_end_mask_0 = const()[name = tensor("op_397_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_397_squeeze_mask_0 = const()[name = tensor("op_397_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_397 = slice_by_index(begin = var_397_begin_0, end = var_397_end_0, end_mask = var_397_end_mask_0, squeeze_mask = var_397_squeeze_mask_0, x = attn1_cache)[name = tensor("op_397")]; + tensor new_v_cache_5_axis_0 = const()[name = tensor("new_v_cache_5_axis_0"), val = tensor(1)]; + tensor new_v_cache_5_mode_0 = const()[name = tensor("new_v_cache_5_mode_0"), val = tensor("update")]; + tensor new_v_cache_5_validate_indices_0 = const()[name = tensor("new_v_cache_5_validate_indices_0"), val = tensor(false)]; + tensor new_v_cache_5 = scatter_along_axis(axis = new_v_cache_5_axis_0, data = var_397, indices = write_indexes, mode = new_v_cache_5_mode_0, updates = squeeze_5, validate_indices = new_v_cache_5_validate_indices_0)[name = tensor("new_v_cache_5")]; + tensor var_400_axis_0 = const()[name = tensor("op_400_axis_0"), val = tensor(0)]; + tensor var_400 = stack(axis = var_400_axis_0, values = (new_k_cache_5, new_v_cache_5))[name = tensor("op_400")]; + tensor var_401 = not_equal(x = new_k_cache_5, y = new_k_cache_5)[name = tensor("op_401")]; + tensor new_k_cache = select(a = var_212, b = new_k_cache_5, cond = var_401)[name = tensor("new_k_cache")]; + tensor var_404 = not_equal(x = new_v_cache_5, y = new_v_cache_5)[name = tensor("op_404")]; + tensor new_v_cache = select(a = var_212, b = new_v_cache_5, cond = var_404)[name = tensor("new_v_cache")]; + tensor var_409 = const()[name = tensor("op_409"), val = tensor([0, 2, 1, 3])]; + tensor var_411 = const()[name = tensor("op_411"), val = tensor([1, 1])]; + tensor var_412 = reshape(shape = var_411, x = attn1_offset)[name = tensor("op_412")]; + tensor var_414_promoted = const()[name = tensor("op_414_promoted"), val = tensor([0x1.ep+3])]; + tensor var_415 = add(x = var_412, y = var_414_promoted)[name = tensor("op_415")]; + tensor last_pos_dtype_0 = const()[name = tensor("last_pos_dtype_0"), val = tensor("int32")]; + tensor last_pos = cast(dtype = last_pos_dtype_0, x = var_415)[name = tensor("cast_45")]; + tensor diff = sub(x = last_pos, y = slot_idx_1)[name = tensor("diff")]; + tensor var_421_div = floor_div(x = diff, y = capacity)[name = tensor("op_421_div")]; + tensor var_421_div_scaled = mul(x = var_421_div, y = capacity)[name = tensor("op_421_div_scaled")]; + tensor var_421 = sub(x = diff, y = var_421_div_scaled)[name = tensor("op_421")]; + tensor pos_k = sub(x = last_pos, y = var_421)[name = tensor("pos_k")]; + tensor var_427_promoted = const()[name = tensor("op_427_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766144)))]; + tensor pos_q = add(x = var_412, y = var_427_promoted)[name = tensor("pos_q")]; + tensor var_431_axes_0 = const()[name = tensor("op_431_axes_0"), val = tensor([2])]; + tensor var_431 = expand_dims(axes = var_431_axes_0, x = pos_q)[name = tensor("op_431")]; + tensor var_433_axes_0 = const()[name = tensor("op_433_axes_0"), val = tensor([1])]; + tensor var_433 = expand_dims(axes = var_433_axes_0, x = pos_k)[name = tensor("op_433")]; + tensor var_434_promoted_dtype_0 = const()[name = tensor("op_434_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_434_promoted = cast(dtype = var_434_promoted_dtype_0, x = var_433)[name = tensor("cast_44")]; + tensor delta = sub(x = var_431, y = var_434_promoted)[name = tensor("delta")]; + tensor valid_5 = greater_equal(x = var_433, y = var_86)[name = tensor("valid_5")]; + tensor var_443 = const()[name = tensor("op_443"), val = tensor([1, 1, 1])]; + tensor var_444 = reshape(shape = var_443, x = attn1_offset)[name = tensor("op_444")]; + tensor var_446_promoted = const()[name = tensor("op_446_promoted"), val = tensor([0x1.ep+3])]; + tensor var_447 = add(x = var_444, y = var_446_promoted)[name = tensor("op_447")]; + tensor var_448 = less_equal(x = var_434_promoted, y = var_447)[name = tensor("op_448")]; + tensor valid = logical_and(x = valid_5, y = var_448)[name = tensor("valid")]; + tensor var_86_promoted_1 = const()[name = tensor("op_86_promoted_1"), val = tensor(0x0p+0)]; + tensor var_450 = greater_equal(x = delta, y = var_86_promoted_1)[name = tensor("op_450")]; + tensor attn_mask_7 = logical_and(x = valid, y = var_450)[name = tensor("attn_mask_7")]; + tensor var_98_promoted_1 = const()[name = tensor("op_98_promoted_1"), val = tensor(0x1.f4p+7)]; + tensor var_452 = less(x = delta, y = var_98_promoted_1)[name = tensor("op_452")]; + tensor attn_mask_9 = logical_and(x = attn_mask_7, y = var_452)[name = tensor("attn_mask_9")]; + tensor attn_mask_axes_0 = const()[name = tensor("attn_mask_axes_0"), val = tensor([1])]; + tensor attn_mask = expand_dims(axes = attn_mask_axes_0, x = attn_mask_9)[name = tensor("attn_mask")]; + tensor var_457_transpose_x_0 = const()[name = tensor("op_457_transpose_x_0"), val = tensor(false)]; + tensor var_457_transpose_y_0 = const()[name = tensor("op_457_transpose_y_0"), val = tensor(false)]; + tensor transpose_8_perm_0 = const()[name = tensor("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_9_perm_0 = const()[name = tensor("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = new_k_cache)[name = tensor("transpose_12")]; + tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = q)[name = tensor("transpose_13")]; + tensor var_457 = matmul(transpose_x = var_457_transpose_x_0, transpose_y = var_457_transpose_y_0, x = transpose_8, y = transpose_9)[name = tensor("op_457")]; + tensor var_458 = const()[name = tensor("op_458"), val = tensor(0x1p-3)]; + tensor attn_7 = mul(x = var_457, y = var_458)[name = tensor("attn_7")]; + tensor var_460 = logical_not(x = attn_mask)[name = tensor("op_460")]; + tensor attn_9 = select(a = var_100, b = attn_7, cond = var_460)[name = tensor("attn_9")]; + tensor attn = softmax(axis = var_91, x = attn_9)[name = tensor("attn")]; + tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; + tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; + tensor v_attn = transpose(perm = var_409, x = new_v_cache)[name = tensor("transpose_14")]; + tensor x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = attn, y = v_attn)[name = tensor("x_11")]; + tensor var_464_perm_0 = const()[name = tensor("op_464_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 16, 512])]; + tensor var_464 = transpose(perm = var_464_perm_0, x = x_11)[name = tensor("transpose_11")]; + tensor input_15 = reshape(shape = var_465, x = var_464)[name = tensor("input_15")]; + tensor x_13 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight, x = input_15)[name = tensor("linear_5")]; + tensor var_474 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale, y = x_13)[name = tensor("op_474")]; + tensor input_17 = add(x = input_13, y = var_474)[name = tensor("input_17")]; + tensor input_19_axes_0 = const()[name = tensor("input_19_axes_0"), val = tensor([-1])]; + tensor input_19 = layer_norm(axes = input_19_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm2_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_1_norm2_weight, x = input_17)[name = tensor("input_19")]; + tensor var_481 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear1_weight, x = input_19)[name = tensor("linear_6")]; + tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("EXACT")]; + tensor input_21 = gelu(mode = input_21_mode_0, x = var_481)[name = tensor("input_21")]; + tensor x_15 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear2_weight, x = input_21)[name = tensor("linear_7")]; + tensor var_487 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale, y = x_15)[name = tensor("op_487")]; + tensor z = add(x = input_17, y = var_487)[name = tensor("z")]; + tensor x_17_perm_0 = const()[name = tensor("x_17_perm_0"), val = tensor([0, 2, 1])]; + tensor var_507 = const()[name = tensor("op_507"), val = tensor(0x1p+0)]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor(-1)]; + tensor input_23_interleave_0 = const()[name = tensor("input_23_interleave_0"), val = tensor(false)]; + tensor x_17 = transpose(perm = x_17_perm_0, x = z)[name = tensor("transpose_10")]; + tensor input_23 = concat(axis = var_508, interleave = input_23_interleave_0, values = (conv0_prev, x_17))[name = tensor("input_23")]; + tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("valid")]; + tensor input_25_strides_0 = const()[name = tensor("input_25_strides_0"), val = tensor([1])]; + tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([0, 0])]; + tensor input_25_dilations_0 = const()[name = tensor("input_25_dilations_0"), val = tensor([1])]; + tensor input_25_groups_0 = const()[name = tensor("input_25_groups_0"), val = tensor(1)]; + tensor input_25 = conv(bias = mimi_decoder_model_0_conv_bias, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = mimi_decoder_model_0_conv_weight, x = input_23)[name = tensor("input_25")]; + tensor var_542_begin_0 = const()[name = tensor("op_542_begin_0"), val = tensor([0, 0, 16])]; + tensor var_542_end_0 = const()[name = tensor("op_542_end_0"), val = tensor([1, 512, 22])]; + tensor var_542_end_mask_0 = const()[name = tensor("op_542_end_mask_0"), val = tensor([true, true, true])]; + tensor var_542 = slice_by_index(begin = var_542_begin_0, end = var_542_end_0, end_mask = var_542_end_mask_0, x = input_23)[name = tensor("op_542")]; + tensor input_27 = elu(alpha = var_507, x = input_25)[name = tensor("input_27")]; + tensor y_5_pad_type_0 = const()[name = tensor("y_5_pad_type_0"), val = tensor("valid")]; + tensor y_5_strides_0 = const()[name = tensor("y_5_strides_0"), val = tensor([6])]; + tensor y_5_pad_0 = const()[name = tensor("y_5_pad_0"), val = tensor([0, 0])]; + tensor y_5_dilations_0 = const()[name = tensor("y_5_dilations_0"), val = tensor([1])]; + tensor y_5_groups_0 = const()[name = tensor("y_5_groups_0"), val = tensor(1)]; + tensor y_5_has_output_shape_output_shape_0 = const()[name = tensor("y_5_has_output_shape_output_shape_0"), val = tensor([1, 256, 102])]; + tensor y_5_has_output_shape = conv_transpose(bias = mimi_decoder_model_2_convtr_bias, dilations = y_5_dilations_0, groups = y_5_groups_0, output_shape = y_5_has_output_shape_output_shape_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = mimi_decoder_model_2_convtr_weight, x = input_27)[name = tensor("y_5_has_output_shape")]; + tensor var_557_begin_0 = const()[name = tensor("op_557_begin_0"), val = tensor([0, 0, 0])]; + tensor var_557_end_0 = const()[name = tensor("op_557_end_0"), val = tensor([1, 256, 6])]; + tensor var_557_end_mask_0 = const()[name = tensor("op_557_end_mask_0"), val = tensor([true, true, false])]; + tensor var_557 = slice_by_index(begin = var_557_begin_0, end = var_557_end_0, end_mask = var_557_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_557")]; + tensor var_558 = add(x = var_557, y = convtr0_partial)[name = tensor("op_558")]; + tensor var_559_begin_0 = const()[name = tensor("op_559_begin_0"), val = tensor([0, 0, 6])]; + tensor var_559_end_0 = const()[name = tensor("op_559_end_0"), val = tensor([1, 256, 102])]; + tensor var_559_end_mask_0 = const()[name = tensor("op_559_end_mask_0"), val = tensor([true, true, true])]; + tensor var_559 = slice_by_index(begin = var_559_begin_0, end = var_559_end_0, end_mask = var_559_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_559")]; + tensor y_7_interleave_0 = const()[name = tensor("y_7_interleave_0"), val = tensor(false)]; + tensor y_7 = concat(axis = var_508, interleave = y_7_interleave_0, values = (var_558, var_559))[name = tensor("y_7")]; + tensor new_partial_1_begin_0 = const()[name = tensor("new_partial_1_begin_0"), val = tensor([0, 0, 96])]; + tensor new_partial_1_end_0 = const()[name = tensor("new_partial_1_end_0"), val = tensor([1, 256, 102])]; + tensor new_partial_1_end_mask_0 = const()[name = tensor("new_partial_1_end_mask_0"), val = tensor([true, true, true])]; + tensor new_partial_1 = slice_by_index(begin = new_partial_1_begin_0, end = new_partial_1_end_0, end_mask = new_partial_1_end_mask_0, x = y_7)[name = tensor("new_partial_1")]; + tensor var_564 = const()[name = tensor("op_564"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766272)))]; + tensor var_565 = sub(x = new_partial_1, y = var_564)[name = tensor("op_565")]; + tensor input_29_begin_0 = const()[name = tensor("input_29_begin_0"), val = tensor([0, 0, 0])]; + tensor input_29_end_0 = const()[name = tensor("input_29_end_0"), val = tensor([1, 256, 96])]; + tensor input_29_end_mask_0 = const()[name = tensor("input_29_end_mask_0"), val = tensor([true, true, false])]; + tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, x = y_7)[name = tensor("input_29")]; + tensor x_19 = elu(alpha = var_507, x = input_29)[name = tensor("x_19")]; + tensor input_31_interleave_0 = const()[name = tensor("input_31_interleave_0"), val = tensor(false)]; + tensor input_31 = concat(axis = var_508, interleave = input_31_interleave_0, values = (res0_conv0_prev, x_19))[name = tensor("input_31")]; + tensor input_33_pad_type_0 = const()[name = tensor("input_33_pad_type_0"), val = tensor("valid")]; + tensor input_33_strides_0 = const()[name = tensor("input_33_strides_0"), val = tensor([1])]; + tensor input_33_pad_0 = const()[name = tensor("input_33_pad_0"), val = tensor([0, 0])]; + tensor input_33_dilations_0 = const()[name = tensor("input_33_dilations_0"), val = tensor([1])]; + tensor input_33_groups_0 = const()[name = tensor("input_33_groups_0"), val = tensor(1)]; + tensor input_33 = conv(bias = mimi_decoder_model_3_block_1_conv_bias, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = mimi_decoder_model_3_block_1_conv_weight, x = input_31)[name = tensor("input_33")]; + tensor var_585_begin_0 = const()[name = tensor("op_585_begin_0"), val = tensor([0, 0, 96])]; + tensor var_585_end_0 = const()[name = tensor("op_585_end_0"), val = tensor([1, 256, 98])]; + tensor var_585_end_mask_0 = const()[name = tensor("op_585_end_mask_0"), val = tensor([true, true, true])]; + tensor var_585 = slice_by_index(begin = var_585_begin_0, end = var_585_end_0, end_mask = var_585_end_mask_0, x = input_31)[name = tensor("op_585")]; + tensor x_21 = elu(alpha = var_507, x = input_33)[name = tensor("x_21")]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("valid")]; + tensor v_5_strides_0 = const()[name = tensor("v_5_strides_0"), val = tensor([1])]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0])]; + tensor v_5_dilations_0 = const()[name = tensor("v_5_dilations_0"), val = tensor([1])]; + tensor v_5_groups_0 = const()[name = tensor("v_5_groups_0"), val = tensor(1)]; + tensor v_5 = conv(bias = mimi_decoder_model_3_block_3_conv_bias, dilations = v_5_dilations_0, groups = v_5_groups_0, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = v_5_strides_0, weight = mimi_decoder_model_3_block_3_conv_weight, x = x_21)[name = tensor("v_5")]; + tensor input_35 = add(x = input_29, y = v_5)[name = tensor("input_35")]; + tensor input_37 = elu(alpha = var_507, x = input_35)[name = tensor("input_37")]; + tensor y_9_pad_type_0 = const()[name = tensor("y_9_pad_type_0"), val = tensor("valid")]; + tensor y_9_strides_0 = const()[name = tensor("y_9_strides_0"), val = tensor([5])]; + tensor y_9_pad_0 = const()[name = tensor("y_9_pad_0"), val = tensor([0, 0])]; + tensor y_9_dilations_0 = const()[name = tensor("y_9_dilations_0"), val = tensor([1])]; + tensor y_9_groups_0 = const()[name = tensor("y_9_groups_0"), val = tensor(1)]; + tensor y_9_has_output_shape_output_shape_0 = const()[name = tensor("y_9_has_output_shape_output_shape_0"), val = tensor([1, 128, 485])]; + tensor y_9_has_output_shape = conv_transpose(bias = mimi_decoder_model_5_convtr_bias, dilations = y_9_dilations_0, groups = y_9_groups_0, output_shape = y_9_has_output_shape_output_shape_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = mimi_decoder_model_5_convtr_weight, x = input_37)[name = tensor("y_9_has_output_shape")]; + tensor var_613_begin_0 = const()[name = tensor("op_613_begin_0"), val = tensor([0, 0, 0])]; + tensor var_613_end_0 = const()[name = tensor("op_613_end_0"), val = tensor([1, 128, 5])]; + tensor var_613_end_mask_0 = const()[name = tensor("op_613_end_mask_0"), val = tensor([true, true, false])]; + tensor var_613 = slice_by_index(begin = var_613_begin_0, end = var_613_end_0, end_mask = var_613_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_613")]; + tensor var_614 = add(x = var_613, y = convtr1_partial)[name = tensor("op_614")]; + tensor var_615_begin_0 = const()[name = tensor("op_615_begin_0"), val = tensor([0, 0, 5])]; + tensor var_615_end_0 = const()[name = tensor("op_615_end_0"), val = tensor([1, 128, 485])]; + tensor var_615_end_mask_0 = const()[name = tensor("op_615_end_mask_0"), val = tensor([true, true, true])]; + tensor var_615 = slice_by_index(begin = var_615_begin_0, end = var_615_end_0, end_mask = var_615_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_615")]; + tensor y_11_interleave_0 = const()[name = tensor("y_11_interleave_0"), val = tensor(false)]; + tensor y_11 = concat(axis = var_508, interleave = y_11_interleave_0, values = (var_614, var_615))[name = tensor("y_11")]; + tensor new_partial_3_begin_0 = const()[name = tensor("new_partial_3_begin_0"), val = tensor([0, 0, 480])]; + tensor new_partial_3_end_0 = const()[name = tensor("new_partial_3_end_0"), val = tensor([1, 128, 485])]; + tensor new_partial_3_end_mask_0 = const()[name = tensor("new_partial_3_end_mask_0"), val = tensor([true, true, true])]; + tensor new_partial_3 = slice_by_index(begin = new_partial_3_begin_0, end = new_partial_3_end_0, end_mask = new_partial_3_end_mask_0, x = y_11)[name = tensor("new_partial_3")]; + tensor var_620 = const()[name = tensor("op_620"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41767360)))]; + tensor var_621 = sub(x = new_partial_3, y = var_620)[name = tensor("op_621")]; + tensor input_39_begin_0 = const()[name = tensor("input_39_begin_0"), val = tensor([0, 0, 0])]; + tensor input_39_end_0 = const()[name = tensor("input_39_end_0"), val = tensor([1, 128, 480])]; + tensor input_39_end_mask_0 = const()[name = tensor("input_39_end_mask_0"), val = tensor([true, true, false])]; + tensor input_39 = slice_by_index(begin = input_39_begin_0, end = input_39_end_0, end_mask = input_39_end_mask_0, x = y_11)[name = tensor("input_39")]; + tensor x_23 = elu(alpha = var_507, x = input_39)[name = tensor("x_23")]; + tensor input_41_interleave_0 = const()[name = tensor("input_41_interleave_0"), val = tensor(false)]; + tensor input_41 = concat(axis = var_508, interleave = input_41_interleave_0, values = (res1_conv0_prev, x_23))[name = tensor("input_41")]; + tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; + tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; + tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; + tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; + tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; + tensor input_43 = conv(bias = mimi_decoder_model_6_block_1_conv_bias, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = mimi_decoder_model_6_block_1_conv_weight, x = input_41)[name = tensor("input_43")]; + tensor var_641_begin_0 = const()[name = tensor("op_641_begin_0"), val = tensor([0, 0, 480])]; + tensor var_641_end_0 = const()[name = tensor("op_641_end_0"), val = tensor([1, 128, 482])]; + tensor var_641_end_mask_0 = const()[name = tensor("op_641_end_mask_0"), val = tensor([true, true, true])]; + tensor var_641 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, x = input_41)[name = tensor("op_641")]; + tensor x_25 = elu(alpha = var_507, x = input_43)[name = tensor("x_25")]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("valid")]; + tensor v_7_strides_0 = const()[name = tensor("v_7_strides_0"), val = tensor([1])]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0])]; + tensor v_7_dilations_0 = const()[name = tensor("v_7_dilations_0"), val = tensor([1])]; + tensor v_7_groups_0 = const()[name = tensor("v_7_groups_0"), val = tensor(1)]; + tensor v_7 = conv(bias = mimi_decoder_model_6_block_3_conv_bias, dilations = v_7_dilations_0, groups = v_7_groups_0, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = v_7_strides_0, weight = mimi_decoder_model_6_block_3_conv_weight, x = x_25)[name = tensor("v_7")]; + tensor input_45 = add(x = input_39, y = v_7)[name = tensor("input_45")]; + tensor input_47 = elu(alpha = var_507, x = input_45)[name = tensor("input_47")]; + tensor y_13_pad_type_0 = const()[name = tensor("y_13_pad_type_0"), val = tensor("valid")]; + tensor y_13_strides_0 = const()[name = tensor("y_13_strides_0"), val = tensor([4])]; + tensor y_13_pad_0 = const()[name = tensor("y_13_pad_0"), val = tensor([0, 0])]; + tensor y_13_dilations_0 = const()[name = tensor("y_13_dilations_0"), val = tensor([1])]; + tensor y_13_groups_0 = const()[name = tensor("y_13_groups_0"), val = tensor(1)]; + tensor y_13_has_output_shape_output_shape_0 = const()[name = tensor("y_13_has_output_shape_output_shape_0"), val = tensor([1, 64, 1924])]; + tensor y_13_has_output_shape = conv_transpose(bias = mimi_decoder_model_8_convtr_bias, dilations = y_13_dilations_0, groups = y_13_groups_0, output_shape = y_13_has_output_shape_output_shape_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = mimi_decoder_model_8_convtr_weight, x = input_47)[name = tensor("y_13_has_output_shape")]; + tensor var_669_begin_0 = const()[name = tensor("op_669_begin_0"), val = tensor([0, 0, 0])]; + tensor var_669_end_0 = const()[name = tensor("op_669_end_0"), val = tensor([1, 64, 4])]; + tensor var_669_end_mask_0 = const()[name = tensor("op_669_end_mask_0"), val = tensor([true, true, false])]; + tensor var_669 = slice_by_index(begin = var_669_begin_0, end = var_669_end_0, end_mask = var_669_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_669")]; + tensor var_670 = add(x = var_669, y = convtr2_partial)[name = tensor("op_670")]; + tensor var_671_begin_0 = const()[name = tensor("op_671_begin_0"), val = tensor([0, 0, 4])]; + tensor var_671_end_0 = const()[name = tensor("op_671_end_0"), val = tensor([1, 64, 1924])]; + tensor var_671_end_mask_0 = const()[name = tensor("op_671_end_mask_0"), val = tensor([true, true, true])]; + tensor var_671 = slice_by_index(begin = var_671_begin_0, end = var_671_end_0, end_mask = var_671_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_671")]; + tensor y_interleave_0 = const()[name = tensor("y_interleave_0"), val = tensor(false)]; + tensor y = concat(axis = var_508, interleave = y_interleave_0, values = (var_670, var_671))[name = tensor("y")]; + tensor new_partial_begin_0 = const()[name = tensor("new_partial_begin_0"), val = tensor([0, 0, 1920])]; + tensor new_partial_end_0 = const()[name = tensor("new_partial_end_0"), val = tensor([1, 64, 1924])]; + tensor new_partial_end_mask_0 = const()[name = tensor("new_partial_end_mask_0"), val = tensor([true, true, true])]; + tensor new_partial = slice_by_index(begin = new_partial_begin_0, end = new_partial_end_0, end_mask = new_partial_end_mask_0, x = y)[name = tensor("new_partial")]; + tensor var_676 = const()[name = tensor("op_676"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41767936)))]; + tensor var_677 = sub(x = new_partial, y = var_676)[name = tensor("op_677")]; + tensor input_49_begin_0 = const()[name = tensor("input_49_begin_0"), val = tensor([0, 0, 0])]; + tensor input_49_end_0 = const()[name = tensor("input_49_end_0"), val = tensor([1, 64, 1920])]; + tensor input_49_end_mask_0 = const()[name = tensor("input_49_end_mask_0"), val = tensor([true, true, false])]; + tensor input_49 = slice_by_index(begin = input_49_begin_0, end = input_49_end_0, end_mask = input_49_end_mask_0, x = y)[name = tensor("input_49")]; + tensor x_27 = elu(alpha = var_507, x = input_49)[name = tensor("x_27")]; + tensor input_51_interleave_0 = const()[name = tensor("input_51_interleave_0"), val = tensor(false)]; + tensor input_51 = concat(axis = var_508, interleave = input_51_interleave_0, values = (res2_conv0_prev, x_27))[name = tensor("input_51")]; + tensor input_53_pad_type_0 = const()[name = tensor("input_53_pad_type_0"), val = tensor("valid")]; + tensor input_53_strides_0 = const()[name = tensor("input_53_strides_0"), val = tensor([1])]; + tensor input_53_pad_0 = const()[name = tensor("input_53_pad_0"), val = tensor([0, 0])]; + tensor input_53_dilations_0 = const()[name = tensor("input_53_dilations_0"), val = tensor([1])]; + tensor input_53_groups_0 = const()[name = tensor("input_53_groups_0"), val = tensor(1)]; + tensor input_53 = conv(bias = mimi_decoder_model_9_block_1_conv_bias, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = mimi_decoder_model_9_block_1_conv_weight, x = input_51)[name = tensor("input_53")]; + tensor var_697_begin_0 = const()[name = tensor("op_697_begin_0"), val = tensor([0, 0, 1920])]; + tensor var_697_end_0 = const()[name = tensor("op_697_end_0"), val = tensor([1, 64, 1922])]; + tensor var_697_end_mask_0 = const()[name = tensor("op_697_end_mask_0"), val = tensor([true, true, true])]; + tensor var_697 = slice_by_index(begin = var_697_begin_0, end = var_697_end_0, end_mask = var_697_end_mask_0, x = input_51)[name = tensor("op_697")]; + tensor x_29 = elu(alpha = var_507, x = input_53)[name = tensor("x_29")]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("valid")]; + tensor v_strides_0 = const()[name = tensor("v_strides_0"), val = tensor([1])]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0])]; + tensor v_dilations_0 = const()[name = tensor("v_dilations_0"), val = tensor([1])]; + tensor v_groups_0 = const()[name = tensor("v_groups_0"), val = tensor(1)]; + tensor v = conv(bias = mimi_decoder_model_9_block_3_conv_bias, dilations = v_dilations_0, groups = v_groups_0, pad = v_pad_0, pad_type = v_pad_type_0, strides = v_strides_0, weight = mimi_decoder_model_9_block_3_conv_weight, x = x_29)[name = tensor("v")]; + tensor input_55 = add(x = input_49, y = v)[name = tensor("input_55")]; + tensor x = elu(alpha = var_507, x = input_55)[name = tensor("x")]; + tensor input_interleave_0 = const()[name = tensor("input_interleave_0"), val = tensor(false)]; + tensor input = concat(axis = var_508, interleave = input_interleave_0, values = (conv_final_prev, x))[name = tensor("input")]; + tensor var_724_pad_type_0 = const()[name = tensor("op_724_pad_type_0"), val = tensor("valid")]; + tensor var_724_strides_0 = const()[name = tensor("op_724_strides_0"), val = tensor([1])]; + tensor var_724_pad_0 = const()[name = tensor("op_724_pad_0"), val = tensor([0, 0])]; + tensor var_724_dilations_0 = const()[name = tensor("op_724_dilations_0"), val = tensor([1])]; + tensor var_724_groups_0 = const()[name = tensor("op_724_groups_0"), val = tensor(1)]; + tensor var_724 = conv(bias = mimi_decoder_model_11_conv_bias, dilations = var_724_dilations_0, groups = var_724_groups_0, pad = var_724_pad_0, pad_type = var_724_pad_type_0, strides = var_724_strides_0, weight = mimi_decoder_model_11_conv_weight, x = input)[name = tensor("op_724")]; + tensor var_725_begin_0 = const()[name = tensor("op_725_begin_0"), val = tensor([0, 0, 1920])]; + tensor var_725_end_0 = const()[name = tensor("op_725_end_0"), val = tensor([1, 64, 1922])]; + tensor var_725_end_mask_0 = const()[name = tensor("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = input)[name = tensor("op_725")]; + tensor var_740_promoted = const()[name = tensor("op_740_promoted"), val = tensor(0x1p+4)]; + tensor var_741 = add(x = attn0_offset, y = var_740_promoted)[name = tensor("op_741")]; + tensor var_743_promoted = const()[name = tensor("op_743_promoted"), val = tensor(0x1p+4)]; + tensor var_744 = add(x = attn1_offset, y = var_743_promoted)[name = tensor("op_744")]; + tensor conv0_first_tmp = identity(x = conv0_first)[name = tensor("conv0_first_tmp")]; + tensor res0_conv0_first_tmp = identity(x = res0_conv0_first)[name = tensor("res0_conv0_first_tmp")]; + tensor res0_conv1_prev_tmp = identity(x = res0_conv1_prev)[name = tensor("res0_conv1_prev_tmp")]; + tensor res0_conv1_first_tmp = identity(x = res0_conv1_first)[name = tensor("res0_conv1_first_tmp")]; + tensor res1_conv0_first_tmp = identity(x = res1_conv0_first)[name = tensor("res1_conv0_first_tmp")]; + tensor res1_conv1_prev_tmp = identity(x = res1_conv1_prev)[name = tensor("res1_conv1_prev_tmp")]; + tensor res1_conv1_first_tmp = identity(x = res1_conv1_first)[name = tensor("res1_conv1_first_tmp")]; + tensor res2_conv0_first_tmp = identity(x = res2_conv0_first)[name = tensor("res2_conv0_first_tmp")]; + tensor res2_conv1_prev_tmp = identity(x = res2_conv1_prev)[name = tensor("res2_conv1_prev_tmp")]; + tensor res2_conv1_first_tmp = identity(x = res2_conv1_first)[name = tensor("res2_conv1_first_tmp")]; + tensor conv_final_first_tmp = identity(x = conv_final_first)[name = tensor("conv_final_first_tmp")]; + } -> (var_724, var_77, var_210, var_741, var_400, var_744, var_542, conv0_first, var_565, var_585, res0_conv0_first, res0_conv1_prev, res0_conv1_first, var_621, var_641, res1_conv0_first, res1_conv1_prev, res1_conv1_first, var_677, var_697, res2_conv0_first, res2_conv1_prev, res2_conv1_first, var_725, conv_final_first); +} \ No newline at end of file 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{"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor cache0, tensor cache1, tensor cache10, tensor cache11, tensor cache12, tensor cache13, tensor cache14, tensor cache15, tensor cache16, tensor cache17, tensor cache18, tensor cache19, tensor cache2, tensor cache20, tensor cache21, tensor cache22, tensor cache23, tensor cache3, tensor cache4, tensor cache5, tensor cache6, tensor cache7, tensor cache8, tensor cache9, tensor conditioning, tensor position0, tensor position1, tensor position10, tensor position11, tensor position12, tensor position13, tensor position14, tensor position15, tensor position16, tensor position17, tensor position18, tensor position19, tensor position2, tensor position20, tensor position21, tensor position22, tensor position23, tensor position3, tensor position4, tensor position5, tensor position6, tensor position7, tensor position8, tensor position9) { + tensor norm0_1_bias = const()[name = tensor("norm0_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor norm0_1_weight = const()[name = tensor("norm0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4224)))]; + tensor attn0_in_proj_weight = const()[name = tensor("attn0_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8384)))]; + tensor attn0_out_proj_weight = const()[name = tensor("attn0_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12591360)))]; + tensor norm0_2_bias = const()[name = tensor("norm0_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16785728)))]; + tensor norm0_2_weight = const()[name = tensor("norm0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16789888)))]; + tensor linear0_1_weight = const()[name = tensor("linear0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16794048)))]; + tensor linear0_2_weight = const()[name = tensor("linear0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33571328)))]; + tensor norm1_1_bias = const()[name = tensor("norm1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50348608)))]; + tensor norm1_1_weight = const()[name = tensor("norm1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50352768)))]; + tensor attn1_in_proj_weight = const()[name = tensor("attn1_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50356928)))]; + tensor attn1_out_proj_weight = const()[name = tensor("attn1_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62939904)))]; + tensor norm1_2_bias = const()[name = tensor("norm1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67134272)))]; + tensor norm1_2_weight = const()[name = tensor("norm1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67138432)))]; + tensor linear1_1_weight = const()[name = tensor("linear1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67142592)))]; + tensor linear1_2_weight = const()[name = tensor("linear1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83919872)))]; + tensor norm2_1_bias = const()[name = tensor("norm2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100697152)))]; + tensor norm2_1_weight = const()[name = tensor("norm2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100701312)))]; + tensor attn2_in_proj_weight = const()[name = tensor("attn2_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100705472)))]; + tensor attn2_out_proj_weight = const()[name = tensor("attn2_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113288448)))]; + tensor norm2_2_bias = const()[name = tensor("norm2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117482816)))]; + tensor norm2_2_weight = const()[name = tensor("norm2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117486976)))]; + tensor linear2_1_weight = const()[name = tensor("linear2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117491136)))]; + tensor linear2_2_weight = const()[name = tensor("linear2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134268416)))]; + tensor norm3_1_bias = const()[name = tensor("norm3_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151045696)))]; + tensor norm3_1_weight = const()[name = tensor("norm3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151049856)))]; + tensor attn3_in_proj_weight = const()[name = tensor("attn3_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151054016)))]; + tensor attn3_out_proj_weight = const()[name = tensor("attn3_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163636992)))]; + tensor norm3_2_bias = const()[name = tensor("norm3_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167831360)))]; + tensor norm3_2_weight = const()[name = tensor("norm3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167835520)))]; + tensor linear3_1_weight = const()[name = tensor("linear3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167839680)))]; + tensor linear3_2_weight = const()[name = tensor("linear3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184616960)))]; + tensor norm4_1_bias = const()[name = tensor("norm4_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201394240)))]; + tensor norm4_1_weight = const()[name = tensor("norm4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201398400)))]; + tensor attn4_in_proj_weight = const()[name = tensor("attn4_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201402560)))]; + tensor attn4_out_proj_weight = const()[name = tensor("attn4_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213985536)))]; + tensor norm4_2_bias = const()[name = tensor("norm4_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218179904)))]; + tensor norm4_2_weight = const()[name = tensor("norm4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218184064)))]; + tensor linear4_1_weight = const()[name = tensor("linear4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218188224)))]; + tensor linear4_2_weight = const()[name = tensor("linear4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234965504)))]; + tensor norm5_1_bias = const()[name = tensor("norm5_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251742784)))]; + tensor norm5_1_weight = const()[name = tensor("norm5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251746944)))]; + tensor attn5_in_proj_weight = const()[name = tensor("attn5_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251751104)))]; + tensor attn5_out_proj_weight = const()[name = tensor("attn5_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264334080)))]; + tensor norm5_2_bias = const()[name = tensor("norm5_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268528448)))]; + tensor norm5_2_weight = const()[name = tensor("norm5_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268532608)))]; + tensor linear5_1_weight = const()[name = tensor("linear5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268536768)))]; + tensor linear5_2_weight = const()[name = tensor("linear5_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285314048)))]; + tensor norm6_1_bias = const()[name = tensor("norm6_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302091328)))]; + tensor norm6_1_weight = const()[name = tensor("norm6_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302095488)))]; + tensor attn6_in_proj_weight = const()[name = tensor("attn6_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302099648)))]; + tensor attn6_out_proj_weight = const()[name = tensor("attn6_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314682624)))]; + tensor norm6_2_bias = const()[name = tensor("norm6_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318876992)))]; + tensor norm6_2_weight = const()[name = tensor("norm6_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318881152)))]; + tensor linear6_1_weight = const()[name = tensor("linear6_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318885312)))]; + tensor linear6_2_weight = const()[name = tensor("linear6_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335662592)))]; + tensor norm7_1_bias = const()[name = tensor("norm7_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(352439872)))]; + tensor norm7_1_weight = const()[name = tensor("norm7_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(352444032)))]; + tensor attn7_in_proj_weight = const()[name = tensor("attn7_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(352448192)))]; + tensor attn7_out_proj_weight = const()[name = tensor("attn7_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365031168)))]; + tensor norm7_2_bias = const()[name = tensor("norm7_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369225536)))]; + tensor norm7_2_weight = const()[name = tensor("norm7_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369229696)))]; + tensor linear7_1_weight = const()[name = tensor("linear7_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369233856)))]; + tensor linear7_2_weight = const()[name = tensor("linear7_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386011136)))]; + tensor norm8_1_bias = const()[name = tensor("norm8_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402788416)))]; + tensor norm8_1_weight = const()[name = tensor("norm8_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402792576)))]; + tensor attn8_in_proj_weight = const()[name = tensor("attn8_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402796736)))]; + tensor attn8_out_proj_weight = const()[name = tensor("attn8_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415379712)))]; + tensor norm8_2_bias = const()[name = tensor("norm8_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419574080)))]; + tensor norm8_2_weight = const()[name = tensor("norm8_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419578240)))]; + tensor linear8_1_weight = const()[name = tensor("linear8_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419582400)))]; + tensor linear8_2_weight = const()[name = tensor("linear8_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436359680)))]; + tensor norm9_1_bias = const()[name = tensor("norm9_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(453136960)))]; + tensor norm9_1_weight = const()[name = tensor("norm9_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(453141120)))]; + tensor attn9_in_proj_weight = const()[name = tensor("attn9_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(453145280)))]; + tensor attn9_out_proj_weight = const()[name = tensor("attn9_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465728256)))]; + tensor norm9_2_bias = const()[name = tensor("norm9_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469922624)))]; + tensor norm9_2_weight = const()[name = tensor("norm9_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469926784)))]; + tensor linear9_1_weight = const()[name = tensor("linear9_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469930944)))]; + tensor linear9_2_weight = const()[name = tensor("linear9_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486708224)))]; + tensor norm10_1_bias = const()[name = tensor("norm10_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503485504)))]; + tensor norm10_1_weight = const()[name = tensor("norm10_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503489664)))]; + tensor attn10_in_proj_weight = const()[name = tensor("attn10_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503493824)))]; + tensor attn10_out_proj_weight = const()[name = tensor("attn10_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(516076800)))]; + tensor norm10_2_bias = const()[name = tensor("norm10_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520271168)))]; + tensor norm10_2_weight = const()[name = tensor("norm10_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520275328)))]; + tensor linear10_1_weight = const()[name = tensor("linear10_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520279488)))]; + tensor linear10_2_weight = const()[name = tensor("linear10_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537056768)))]; + tensor norm11_1_bias = const()[name = tensor("norm11_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553834048)))]; + tensor norm11_1_weight = const()[name = tensor("norm11_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553838208)))]; + tensor attn11_in_proj_weight = const()[name = tensor("attn11_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553842368)))]; + tensor attn11_out_proj_weight = const()[name = tensor("attn11_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566425344)))]; + tensor norm11_2_bias = const()[name = tensor("norm11_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570619712)))]; + tensor norm11_2_weight = const()[name = tensor("norm11_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570623872)))]; + tensor linear11_1_weight = const()[name = tensor("linear11_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570628032)))]; + tensor linear11_2_weight = const()[name = tensor("linear11_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(587405312)))]; + tensor norm12_1_bias = const()[name = tensor("norm12_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(604182592)))]; + tensor norm12_1_weight = const()[name = tensor("norm12_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(604186752)))]; + tensor attn12_in_proj_weight = const()[name = tensor("attn12_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(604190912)))]; + tensor attn12_out_proj_weight = const()[name = tensor("attn12_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(616773888)))]; + tensor norm12_2_bias = const()[name = tensor("norm12_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620968256)))]; + tensor norm12_2_weight = const()[name = tensor("norm12_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620972416)))]; + tensor linear12_1_weight = const()[name = tensor("linear12_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620976576)))]; + tensor linear12_2_weight = const()[name = tensor("linear12_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637753856)))]; + tensor norm13_1_bias = const()[name = tensor("norm13_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654531136)))]; + tensor norm13_1_weight = const()[name = tensor("norm13_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654535296)))]; + tensor attn13_in_proj_weight = const()[name = tensor("attn13_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654539456)))]; + tensor attn13_out_proj_weight = const()[name = tensor("attn13_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(667122432)))]; + tensor norm13_2_bias = const()[name = tensor("norm13_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671316800)))]; + tensor norm13_2_weight = const()[name = tensor("norm13_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671320960)))]; + tensor linear13_1_weight = const()[name = tensor("linear13_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671325120)))]; + tensor linear13_2_weight = const()[name = tensor("linear13_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688102400)))]; + tensor norm14_1_bias = const()[name = tensor("norm14_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704879680)))]; + tensor norm14_1_weight = const()[name = tensor("norm14_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704883840)))]; + tensor attn14_in_proj_weight = const()[name = tensor("attn14_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704888000)))]; + tensor attn14_out_proj_weight = const()[name = tensor("attn14_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717470976)))]; + tensor norm14_2_bias = const()[name = tensor("norm14_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721665344)))]; + tensor norm14_2_weight = const()[name = tensor("norm14_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721669504)))]; + tensor linear14_1_weight = const()[name = tensor("linear14_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721673664)))]; + tensor linear14_2_weight = const()[name = tensor("linear14_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(738450944)))]; + tensor norm15_1_bias = const()[name = tensor("norm15_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755228224)))]; + tensor norm15_1_weight = const()[name = tensor("norm15_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755232384)))]; + tensor attn15_in_proj_weight = const()[name = tensor("attn15_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755236544)))]; + tensor attn15_out_proj_weight = const()[name = tensor("attn15_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767819520)))]; + tensor norm15_2_bias = const()[name = tensor("norm15_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772013888)))]; + tensor norm15_2_weight = const()[name = tensor("norm15_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772018048)))]; + tensor linear15_1_weight = const()[name = tensor("linear15_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772022208)))]; + tensor linear15_2_weight = const()[name = tensor("linear15_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788799488)))]; + tensor norm16_1_bias = const()[name = tensor("norm16_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805576768)))]; + tensor norm16_1_weight = const()[name = tensor("norm16_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805580928)))]; + tensor attn16_in_proj_weight = const()[name = tensor("attn16_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805585088)))]; + tensor attn16_out_proj_weight = const()[name = tensor("attn16_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818168064)))]; + tensor norm16_2_bias = const()[name = tensor("norm16_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822362432)))]; + tensor norm16_2_weight = const()[name = tensor("norm16_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822366592)))]; + tensor linear16_1_weight = const()[name = tensor("linear16_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822370752)))]; + tensor linear16_2_weight = const()[name = tensor("linear16_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(839148032)))]; + tensor norm17_1_bias = const()[name = tensor("norm17_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(855925312)))]; + tensor norm17_1_weight = const()[name = tensor("norm17_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(855929472)))]; + tensor attn17_in_proj_weight = const()[name = tensor("attn17_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(855933632)))]; + tensor attn17_out_proj_weight = const()[name = tensor("attn17_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(868516608)))]; + tensor norm17_2_bias = const()[name = tensor("norm17_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(872710976)))]; + tensor norm17_2_weight = const()[name = tensor("norm17_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(872715136)))]; + tensor linear17_1_weight = const()[name = tensor("linear17_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(872719296)))]; + tensor linear17_2_weight = const()[name = tensor("linear17_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(889496576)))]; + tensor norm18_1_bias = const()[name = tensor("norm18_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(906273856)))]; + tensor norm18_1_weight = const()[name = tensor("norm18_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(906278016)))]; + tensor attn18_in_proj_weight = const()[name = tensor("attn18_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(906282176)))]; + tensor attn18_out_proj_weight = const()[name = tensor("attn18_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(918865152)))]; + tensor norm18_2_bias = const()[name = tensor("norm18_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923059520)))]; + tensor norm18_2_weight = const()[name = tensor("norm18_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923063680)))]; + tensor linear18_1_weight = const()[name = tensor("linear18_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923067840)))]; + tensor linear18_2_weight = const()[name = tensor("linear18_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939845120)))]; + tensor norm19_1_bias = const()[name = tensor("norm19_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956622400)))]; + tensor norm19_1_weight = const()[name = tensor("norm19_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956626560)))]; + tensor attn19_in_proj_weight = const()[name = tensor("attn19_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956630720)))]; + tensor attn19_out_proj_weight = const()[name = tensor("attn19_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(969213696)))]; + tensor norm19_2_bias = const()[name = tensor("norm19_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973408064)))]; + tensor norm19_2_weight = const()[name = tensor("norm19_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973412224)))]; + tensor linear19_1_weight = const()[name = tensor("linear19_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973416384)))]; + tensor linear19_2_weight = const()[name = tensor("linear19_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(990193664)))]; + tensor norm20_1_bias = const()[name = tensor("norm20_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1006970944)))]; + tensor norm20_1_weight = const()[name = tensor("norm20_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1006975104)))]; + tensor attn20_in_proj_weight = const()[name = tensor("attn20_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1006979264)))]; + tensor attn20_out_proj_weight = const()[name = tensor("attn20_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1019562240)))]; + tensor norm20_2_bias = const()[name = tensor("norm20_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023756608)))]; + tensor norm20_2_weight = const()[name = tensor("norm20_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023760768)))]; + tensor linear20_1_weight = const()[name = tensor("linear20_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023764928)))]; + tensor linear20_2_weight = const()[name = tensor("linear20_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1040542208)))]; + tensor norm21_1_bias = const()[name = tensor("norm21_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057319488)))]; + tensor norm21_1_weight = const()[name = tensor("norm21_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057323648)))]; + tensor attn21_in_proj_weight = const()[name = tensor("attn21_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057327808)))]; + tensor attn21_out_proj_weight = const()[name = tensor("attn21_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1069910784)))]; + tensor norm21_2_bias = const()[name = tensor("norm21_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074105152)))]; + tensor norm21_2_weight = const()[name = tensor("norm21_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074109312)))]; + tensor linear21_1_weight = const()[name = tensor("linear21_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074113472)))]; + tensor linear21_2_weight = const()[name = tensor("linear21_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1090890752)))]; + tensor norm22_1_bias = const()[name = tensor("norm22_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107668032)))]; + tensor norm22_1_weight = const()[name = tensor("norm22_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107672192)))]; + tensor attn22_in_proj_weight = const()[name = tensor("attn22_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107676352)))]; + tensor attn22_out_proj_weight = const()[name = tensor("attn22_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1120259328)))]; + tensor norm22_2_bias = const()[name = tensor("norm22_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1124453696)))]; + tensor norm22_2_weight = const()[name = tensor("norm22_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1124457856)))]; + tensor linear22_1_weight = const()[name = tensor("linear22_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1124462016)))]; + tensor linear22_2_weight = const()[name = tensor("linear22_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1141239296)))]; + tensor norm23_1_bias = const()[name = tensor("norm23_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158016576)))]; + tensor norm23_1_weight = const()[name = tensor("norm23_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158020736)))]; + tensor attn23_in_proj_weight = const()[name = tensor("attn23_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158024896)))]; + tensor var_191 = const()[name = tensor("op_191"), val = tensor(0x1.4f8b58p-17)]; + tensor x_1_axes_0 = const()[name = tensor("x_1_axes_0"), val = tensor([-1])]; + tensor x_1 = layer_norm(axes = x_1_axes_0, beta = norm0_1_bias, epsilon = var_191, gamma = norm0_1_weight, x = conditioning)[name = tensor("x_1")]; + tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170607872)))]; + tensor var_223 = linear(bias = linear_0_bias_0, weight = attn0_in_proj_weight, x = x_1)[name = tensor("linear_0")]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_1 = reshape(shape = var_227, x = var_223)[name = tensor("qkv_1")]; + tensor q_1_begin_0 = const()[name = tensor("q_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_1_end_0 = const()[name = tensor("q_1_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_1_end_mask_0 = const()[name = tensor("q_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_1_squeeze_mask_0 = const()[name = tensor("q_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_1 = slice_by_index(begin = q_1_begin_0, end = q_1_end_0, end_mask = q_1_end_mask_0, squeeze_mask = q_1_squeeze_mask_0, x = qkv_1)[name = tensor("q_1")]; + tensor k_1_begin_0 = const()[name = tensor("k_1_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_1_end_0 = const()[name = tensor("k_1_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_1_end_mask_0 = const()[name = tensor("k_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_1_squeeze_mask_0 = const()[name = tensor("k_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_1 = slice_by_index(begin = k_1_begin_0, end = k_1_end_0, end_mask = k_1_end_mask_0, squeeze_mask = k_1_squeeze_mask_0, x = qkv_1)[name = tensor("k_1")]; + tensor v_1_begin_0 = const()[name = tensor("v_1_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_1_end_0 = const()[name = tensor("v_1_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_1_end_mask_0 = const()[name = tensor("v_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_1_squeeze_mask_0 = const()[name = tensor("v_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_1 = slice_by_index(begin = v_1_begin_0, end = v_1_end_0, end_mask = v_1_end_mask_0, squeeze_mask = v_1_squeeze_mask_0, x = qkv_1)[name = tensor("v_1")]; + tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170620224)))]; + tensor var_331 = const()[name = tensor("op_331"), val = tensor([1, 1, 1, 1])]; + tensor ts_5 = reshape(shape = var_331, x = position0)[name = tensor("ts_5")]; + tensor var_335 = const()[name = tensor("op_335"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_1 = reshape(shape = var_335, x = q_1)[name = tensor("q_complex_1")]; + tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_1 = reshape(shape = var_339, x = k_1)[name = tensor("k_complex_1")]; + tensor var_343_begin_0 = const()[name = tensor("op_343_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_343_end_0 = const()[name = tensor("op_343_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_343_end_mask_0 = const()[name = tensor("op_343_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_343_squeeze_mask_0 = const()[name = tensor("op_343_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_343 = slice_by_index(begin = var_343_begin_0, end = var_343_end_0, end_mask = var_343_end_mask_0, squeeze_mask = var_343_squeeze_mask_0, x = q_complex_1)[name = tensor("op_343")]; + tensor var_351_begin_0 = const()[name = tensor("op_351_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_351_end_0 = const()[name = tensor("op_351_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_351_end_mask_0 = const()[name = tensor("op_351_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_351_squeeze_mask_0 = const()[name = tensor("op_351_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_351 = slice_by_index(begin = var_351_begin_0, end = var_351_end_0, end_mask = var_351_end_mask_0, squeeze_mask = var_351_squeeze_mask_0, x = q_complex_1)[name = tensor("op_351")]; + tensor var_359_begin_0 = const()[name = tensor("op_359_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_359_end_0 = const()[name = tensor("op_359_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_359_end_mask_0 = const()[name = tensor("op_359_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_359_squeeze_mask_0 = const()[name = tensor("op_359_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_359 = slice_by_index(begin = var_359_begin_0, end = var_359_end_0, end_mask = var_359_end_mask_0, squeeze_mask = var_359_squeeze_mask_0, x = k_complex_1)[name = tensor("op_359")]; + tensor var_367_begin_0 = const()[name = tensor("op_367_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_367_end_0 = const()[name = tensor("op_367_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_367_end_mask_0 = const()[name = tensor("op_367_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_367_squeeze_mask_0 = const()[name = tensor("op_367_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_367 = slice_by_index(begin = var_367_begin_0, end = var_367_end_0, end_mask = var_367_end_mask_0, squeeze_mask = var_367_squeeze_mask_0, x = k_complex_1)[name = tensor("op_367")]; + tensor var_373 = mul(x = freqs_1, y = ts_5)[name = tensor("op_373")]; + tensor rotr_1 = cos(x = var_373)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_373)[name = tensor("roti_1")]; + tensor var_377 = mul(x = var_343, y = rotr_1)[name = tensor("op_377")]; + tensor var_378 = mul(x = var_351, y = roti_1)[name = tensor("op_378")]; + tensor qor_1 = sub(x = var_377, y = var_378)[name = tensor("qor_1")]; + tensor var_381 = mul(x = var_343, y = roti_1)[name = tensor("op_381")]; + tensor var_382 = mul(x = var_351, y = rotr_1)[name = tensor("op_382")]; + tensor qoi_1 = add(x = var_381, y = var_382)[name = tensor("qoi_1")]; + tensor var_385 = mul(x = var_359, y = rotr_1)[name = tensor("op_385")]; + tensor var_386 = mul(x = var_367, y = roti_1)[name = tensor("op_386")]; + tensor kor_1 = sub(x = var_385, y = var_386)[name = tensor("kor_1")]; + tensor var_389 = mul(x = var_359, y = roti_1)[name = tensor("op_389")]; + tensor var_390 = mul(x = var_367, y = rotr_1)[name = tensor("op_390")]; + tensor koi_1 = add(x = var_389, y = var_390)[name = tensor("koi_1")]; + tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; + tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; + tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; + tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; + tensor var_419 = const()[name = tensor("op_419"), val = tensor([1, 1, 16, 64])]; + tensor q_3 = reshape(shape = var_419, x = qo_1)[name = tensor("q_3")]; + tensor var_421 = const()[name = tensor("op_421"), val = tensor([1, 1, 16, 64])]; + tensor k_3 = reshape(shape = var_421, x = ko_1)[name = tensor("k_3")]; + tensor _inversed_443_y_0 = const()[name = tensor("_inversed_443_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_443 = mul(x = ts_5, y = _inversed_443_y_0)[name = tensor("_inversed_443")]; + tensor var_444 = floor(x = _inversed_443)[name = tensor("op_444")]; + tensor var_445 = const()[name = tensor("op_445"), val = tensor(0x1p+9)]; + tensor var_446 = mul(x = var_444, y = var_445)[name = tensor("op_446")]; + tensor write_indices_float_3 = sub(x = ts_5, y = var_446)[name = tensor("write_indices_float_3")]; + tensor var_453_dtype_0 = const()[name = tensor("op_453_dtype_0"), val = tensor("int32")]; + tensor write_indices_1_reps_0 = const()[name = tensor("write_indices_1_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_453 = cast(dtype = var_453_dtype_0, x = write_indices_float_3)[name = tensor("cast_446")]; + tensor write_indices_1 = tile(reps = write_indices_1_reps_0, x = var_453)[name = tensor("write_indices_1")]; + tensor var_461_begin_0 = const()[name = tensor("op_461_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_461_end_0 = const()[name = tensor("op_461_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_461_end_mask_0 = const()[name = tensor("op_461_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_461_squeeze_mask_0 = const()[name = tensor("op_461_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_461 = slice_by_index(begin = var_461_begin_0, end = var_461_end_0, end_mask = var_461_end_mask_0, squeeze_mask = var_461_squeeze_mask_0, x = cache0)[name = tensor("op_461")]; + tensor var_463_axis_0 = const()[name = tensor("op_463_axis_0"), val = tensor(1)]; + tensor var_463_mode_0 = const()[name = tensor("op_463_mode_0"), val = tensor("update")]; + tensor var_463_validate_indices_0 = const()[name = tensor("op_463_validate_indices_0"), val = tensor(false)]; + tensor var_463 = scatter_along_axis(axis = var_463_axis_0, data = var_461, indices = write_indices_1, mode = var_463_mode_0, updates = k_3, validate_indices = var_463_validate_indices_0)[name = tensor("op_463")]; + tensor concat_1 = const()[name = tensor("concat_1"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_2 = const()[name = tensor("concat_2"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_46 = const()[name = tensor("shape_46"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_0 = const()[name = tensor("reduce_prod_0"), val = tensor(1048576)]; + tensor range_1d_0_start_0 = const()[name = tensor("range_1d_0_start_0"), val = tensor(0)]; + tensor range_1d_0_step_0 = const()[name = tensor("range_1d_0_step_0"), val = tensor(1)]; + tensor range_1d_0 = range_1d(end = reduce_prod_0, start = range_1d_0_start_0, step = range_1d_0_step_0)[name = tensor("range_1d_0")]; + tensor reshape_0 = reshape(shape = shape_46, x = range_1d_0)[name = tensor("reshape_0")]; + tensor slice_by_index_0 = slice_by_index(begin = concat_1, begin_mask = new_cache_1_internal_tensor_assign_1_begin_mask_0, end = concat_2, end_mask = new_cache_1_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_1_stride_0, x = reshape_0)[name = tensor("slice_by_index_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([-1])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = slice_by_index_0)[name = tensor("reshape_1")]; + tensor reshape_2_shape_0 = const()[name = tensor("reshape_2_shape_0"), val = tensor([-1])]; + tensor reshape_2 = reshape(shape = reshape_2_shape_0, x = var_463)[name = tensor("reshape_2")]; + tensor reshape_3_shape_0 = const()[name = tensor("reshape_3_shape_0"), val = tensor([-1])]; + tensor reshape_3 = reshape(shape = reshape_3_shape_0, x = cache0)[name = tensor("reshape_3")]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor scatter_0_validate_indices_0 = const()[name = tensor("scatter_0_validate_indices_0"), val = tensor(false)]; + tensor scatter_0 = scatter(axis = scatter_0_axis_0, data = reshape_3, indices = reshape_1, mode = scatter_0_mode_0, updates = reshape_2, validate_indices = scatter_0_validate_indices_0)[name = tensor("scatter_0")]; + tensor reshape_4 = reshape(shape = shape_46, x = scatter_0)[name = tensor("reshape_4")]; + tensor var_471_begin_0 = const()[name = tensor("op_471_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_471_end_0 = const()[name = tensor("op_471_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_471_end_mask_0 = const()[name = tensor("op_471_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_471_squeeze_mask_0 = const()[name = tensor("op_471_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_471 = slice_by_index(begin = var_471_begin_0, end = var_471_end_0, end_mask = var_471_end_mask_0, squeeze_mask = var_471_squeeze_mask_0, x = reshape_4)[name = tensor("op_471")]; + tensor var_473_axis_0 = const()[name = tensor("op_473_axis_0"), val = tensor(1)]; + tensor var_473_mode_0 = const()[name = tensor("op_473_mode_0"), val = tensor("update")]; + tensor var_473_validate_indices_0 = const()[name = tensor("op_473_validate_indices_0"), val = tensor(false)]; + tensor var_473 = scatter_along_axis(axis = var_473_axis_0, data = var_471, indices = write_indices_1, mode = var_473_mode_0, updates = v_1, validate_indices = var_473_validate_indices_0)[name = tensor("op_473")]; + tensor concat_3 = const()[name = tensor("concat_3"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_47 = const()[name = tensor("shape_47"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_1 = const()[name = tensor("reduce_prod_1"), val = tensor(1048576)]; + tensor range_1d_1_start_0 = const()[name = tensor("range_1d_1_start_0"), val = tensor(0)]; + tensor range_1d_1_step_0 = const()[name = tensor("range_1d_1_step_0"), val = tensor(1)]; + tensor range_1d_1 = range_1d(end = reduce_prod_1, start = range_1d_1_start_0, step = range_1d_1_step_0)[name = tensor("range_1d_1")]; + tensor reshape_5 = reshape(shape = shape_47, x = range_1d_1)[name = tensor("reshape_5")]; + tensor slice_by_index_1 = slice_by_index(begin = concat_3, begin_mask = new_cache_1_internal_tensor_assign_2_begin_mask_0, end = concat_4, end_mask = new_cache_1_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_2_stride_0, x = reshape_5)[name = tensor("slice_by_index_1")]; + tensor reshape_6_shape_0 = const()[name = tensor("reshape_6_shape_0"), val = tensor([-1])]; + tensor reshape_6 = reshape(shape = reshape_6_shape_0, x = slice_by_index_1)[name = tensor("reshape_6")]; + tensor reshape_7_shape_0 = const()[name = tensor("reshape_7_shape_0"), val = tensor([-1])]; + tensor reshape_7 = reshape(shape = reshape_7_shape_0, x = var_473)[name = tensor("reshape_7")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([-1])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = reshape_4)[name = tensor("reshape_8")]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor scatter_1_validate_indices_0 = const()[name = tensor("scatter_1_validate_indices_0"), val = tensor(false)]; + tensor scatter_1 = scatter(axis = scatter_1_axis_0, data = reshape_8, indices = reshape_6, mode = scatter_1_mode_0, updates = reshape_7, validate_indices = scatter_1_validate_indices_0)[name = tensor("scatter_1")]; + tensor new_cache_1_internal_tensor_assign_2 = reshape(shape = shape_47, x = scatter_1)[name = tensor("reshape_9")]; + tensor keys_1_begin_0 = const()[name = tensor("keys_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_1_end_0 = const()[name = tensor("keys_1_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_1_end_mask_0 = const()[name = tensor("keys_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_1_squeeze_mask_0 = const()[name = tensor("keys_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_1 = slice_by_index(begin = keys_1_begin_0, end = keys_1_end_0, end_mask = keys_1_end_mask_0, squeeze_mask = keys_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("keys_1")]; + tensor values_1_begin_0 = const()[name = tensor("values_1_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_1_end_0 = const()[name = tensor("values_1_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_1_end_mask_0 = const()[name = tensor("values_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_1_squeeze_mask_0 = const()[name = tensor("values_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_1 = slice_by_index(begin = values_1_begin_0, end = values_1_end_0, end_mask = values_1_end_mask_0, squeeze_mask = values_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("values_1")]; + tensor var_485 = not_equal(x = keys_1, y = keys_1)[name = tensor("op_485")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170620416)))]; + tensor keys_3 = select(a = var_491, b = keys_1, cond = var_485)[name = tensor("keys_3")]; + tensor var_493 = not_equal(x = values_1, y = values_1)[name = tensor("op_493")]; + tensor values_3 = select(a = var_491, b = values_1, cond = var_493)[name = tensor("values_3")]; + tensor var_517 = const()[name = tensor("op_517"), val = tensor([0, 2, 1, 3])]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([1, 1, 1])]; + tensor var_531 = reshape(shape = var_530, x = position0)[name = tensor("op_531")]; + tensor var_548 = const()[name = tensor("op_548"), val = tensor(0x1p+0)]; + tensor valid_len_1 = add(x = var_531, y = var_548)[name = tensor("valid_len_1")]; + tensor k_positions_1_promoted = const()[name = tensor("k_positions_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172717632)))]; + tensor valid_mask_1 = less(x = k_positions_1_promoted, y = valid_len_1)[name = tensor("valid_mask_1")]; + tensor causal_mask_1 = less_equal(x = k_positions_1_promoted, y = var_531)[name = tensor("causal_mask_1")]; + tensor attn_mask_1 = logical_and(x = valid_mask_1, y = causal_mask_1)[name = tensor("attn_mask_1")]; + tensor attn_mask_3_axes_0 = const()[name = tensor("attn_mask_3_axes_0"), val = tensor([1])]; + tensor attn_mask_3 = expand_dims(axes = attn_mask_3_axes_0, x = attn_mask_1)[name = tensor("attn_mask_3")]; + tensor var_560 = const()[name = tensor("op_560"), val = tensor([0x1.fffe5cp-4])]; + tensor var_566_transpose_x_0 = const()[name = tensor("op_566_transpose_x_0"), val = tensor(false)]; + tensor var_566_transpose_y_0 = const()[name = tensor("op_566_transpose_y_0"), val = tensor(false)]; + tensor transpose_69_perm_0 = const()[name = tensor("transpose_69_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_70_perm_0 = const()[name = tensor("transpose_70_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = keys_3)[name = tensor("transpose_204")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = q_3)[name = tensor("transpose_205")]; + tensor var_566 = matmul(transpose_x = var_566_transpose_x_0, transpose_y = var_566_transpose_y_0, x = transpose_69, y = transpose_70)[name = tensor("op_566")]; + tensor attn_weights_1 = mul(x = var_566, y = var_560)[name = tensor("attn_weights_1")]; + tensor var_568 = logical_not(x = attn_mask_3)[name = tensor("op_568")]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_3 = select(a = var_569, b = attn_weights_1, cond = var_568)[name = tensor("attn_weights_3")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor(-1)]; + tensor attn_weights_5 = softmax(axis = var_571, x = attn_weights_3)[name = tensor("attn_weights_5")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor values_5 = transpose(perm = var_517, x = values_3)[name = tensor("transpose_206")]; + tensor attn_output_1 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = attn_weights_5, y = values_5)[name = tensor("attn_output_1")]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([0, 2, 1, 3])]; + tensor var_582 = const()[name = tensor("op_582"), val = tensor([1, 1, 1024])]; + tensor var_580 = transpose(perm = var_579, x = attn_output_1)[name = tensor("transpose_203")]; + tensor input_3 = reshape(shape = var_582, x = var_580)[name = tensor("input_3")]; + tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172719744)))]; + tensor attn_out_1 = linear(bias = linear_1_bias_0, weight = attn0_out_proj_weight, x = input_3)[name = tensor("linear_1")]; + tensor var_588 = const()[name = tensor("op_588"), val = tensor(0x1p+0)]; + tensor var_589 = add(x = position0, y = var_588)[name = tensor("op_589")]; + tensor input_5 = add(x = conditioning, y = attn_out_1)[name = tensor("input_5")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor(0x1.4f8b58p-17)]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7 = layer_norm(axes = input_7_axes_0, beta = norm0_2_bias, epsilon = var_593, gamma = norm0_2_weight, x = input_5)[name = tensor("input_7")]; + tensor linear_2_bias_0 = const()[name = tensor("linear_2_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172723904)))]; + tensor var_601 = linear(bias = linear_2_bias_0, weight = linear0_1_weight, x = input_7)[name = tensor("linear_2")]; + tensor input_9_mode_0 = const()[name = tensor("input_9_mode_0"), val = tensor("EXACT")]; + tensor input_9 = gelu(mode = input_9_mode_0, x = var_601)[name = tensor("input_9")]; + tensor ffn_out_1 = linear(bias = linear_1_bias_0, weight = linear0_2_weight, x = input_9)[name = tensor("linear_3")]; + tensor input_11 = add(x = input_5, y = ffn_out_1)[name = tensor("input_11")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor(0x1.4f8b58p-17)]; + tensor x_3_axes_0 = const()[name = tensor("x_3_axes_0"), val = tensor([-1])]; + tensor x_3 = layer_norm(axes = x_3_axes_0, beta = norm1_1_bias, epsilon = var_610, gamma = norm1_1_weight, x = input_11)[name = tensor("x_3")]; + tensor var_642 = linear(bias = linear_0_bias_0, weight = attn1_in_proj_weight, x = x_3)[name = tensor("linear_4")]; + tensor var_646 = const()[name = tensor("op_646"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_3 = reshape(shape = var_646, x = var_642)[name = tensor("qkv_3")]; + tensor q_7_begin_0 = const()[name = tensor("q_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_7_end_0 = const()[name = tensor("q_7_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_7_end_mask_0 = const()[name = tensor("q_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_7_squeeze_mask_0 = const()[name = tensor("q_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_7 = slice_by_index(begin = q_7_begin_0, end = q_7_end_0, end_mask = q_7_end_mask_0, squeeze_mask = q_7_squeeze_mask_0, x = qkv_3)[name = tensor("q_7")]; + tensor k_5_begin_0 = const()[name = tensor("k_5_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_5_end_0 = const()[name = tensor("k_5_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_5_end_mask_0 = const()[name = tensor("k_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_5_squeeze_mask_0 = const()[name = tensor("k_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_5 = slice_by_index(begin = k_5_begin_0, end = k_5_end_0, end_mask = k_5_end_mask_0, squeeze_mask = k_5_squeeze_mask_0, x = qkv_3)[name = tensor("k_5")]; + tensor v_3_begin_0 = const()[name = tensor("v_3_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_3_end_0 = const()[name = tensor("v_3_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_3_end_mask_0 = const()[name = tensor("v_3_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_3_squeeze_mask_0 = const()[name = tensor("v_3_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_3 = slice_by_index(begin = v_3_begin_0, end = v_3_end_0, end_mask = v_3_end_mask_0, squeeze_mask = v_3_squeeze_mask_0, x = qkv_3)[name = tensor("v_3")]; + tensor freqs_3 = const()[name = tensor("freqs_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172740352)))]; + tensor var_750 = const()[name = tensor("op_750"), val = tensor([1, 1, 1, 1])]; + tensor ts_11 = reshape(shape = var_750, x = position1)[name = tensor("ts_11")]; + tensor var_754 = const()[name = tensor("op_754"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_3 = reshape(shape = var_754, x = q_7)[name = tensor("q_complex_3")]; + tensor var_758 = const()[name = tensor("op_758"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_3 = reshape(shape = var_758, x = k_5)[name = tensor("k_complex_3")]; + tensor var_762_begin_0 = const()[name = tensor("op_762_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_762_end_0 = const()[name = tensor("op_762_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_762_end_mask_0 = const()[name = tensor("op_762_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_762_squeeze_mask_0 = const()[name = tensor("op_762_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_762 = slice_by_index(begin = var_762_begin_0, end = var_762_end_0, end_mask = var_762_end_mask_0, squeeze_mask = var_762_squeeze_mask_0, x = q_complex_3)[name = tensor("op_762")]; + tensor var_770_begin_0 = const()[name = tensor("op_770_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_770_end_0 = const()[name = tensor("op_770_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_770_end_mask_0 = const()[name = tensor("op_770_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_770_squeeze_mask_0 = const()[name = tensor("op_770_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_770 = slice_by_index(begin = var_770_begin_0, end = var_770_end_0, end_mask = var_770_end_mask_0, squeeze_mask = var_770_squeeze_mask_0, x = q_complex_3)[name = tensor("op_770")]; + tensor var_778_begin_0 = const()[name = tensor("op_778_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_778_end_0 = const()[name = tensor("op_778_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_778_end_mask_0 = const()[name = tensor("op_778_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_778_squeeze_mask_0 = const()[name = tensor("op_778_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_778 = slice_by_index(begin = var_778_begin_0, end = var_778_end_0, end_mask = var_778_end_mask_0, squeeze_mask = var_778_squeeze_mask_0, x = k_complex_3)[name = tensor("op_778")]; + tensor var_786_begin_0 = const()[name = tensor("op_786_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_786_end_0 = const()[name = tensor("op_786_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_786_end_mask_0 = const()[name = tensor("op_786_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_786_squeeze_mask_0 = const()[name = tensor("op_786_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_786 = slice_by_index(begin = var_786_begin_0, end = var_786_end_0, end_mask = var_786_end_mask_0, squeeze_mask = var_786_squeeze_mask_0, x = k_complex_3)[name = tensor("op_786")]; + tensor var_792 = mul(x = freqs_3, y = ts_11)[name = tensor("op_792")]; + tensor rotr_3 = cos(x = var_792)[name = tensor("rotr_3")]; + tensor roti_3 = sin(x = var_792)[name = tensor("roti_3")]; + tensor var_796 = mul(x = var_762, y = rotr_3)[name = tensor("op_796")]; + tensor var_797 = mul(x = var_770, y = roti_3)[name = tensor("op_797")]; + tensor qor_5 = sub(x = var_796, y = var_797)[name = tensor("qor_5")]; + tensor var_800 = mul(x = var_762, y = roti_3)[name = tensor("op_800")]; + tensor var_801 = mul(x = var_770, y = rotr_3)[name = tensor("op_801")]; + tensor qoi_5 = add(x = var_800, y = var_801)[name = tensor("qoi_5")]; + tensor var_804 = mul(x = var_778, y = rotr_3)[name = tensor("op_804")]; + tensor var_805 = mul(x = var_786, y = roti_3)[name = tensor("op_805")]; + tensor kor_5 = sub(x = var_804, y = var_805)[name = tensor("kor_5")]; + tensor var_808 = mul(x = var_778, y = roti_3)[name = tensor("op_808")]; + tensor var_809 = mul(x = var_786, y = rotr_3)[name = tensor("op_809")]; + tensor koi_5 = add(x = var_808, y = var_809)[name = tensor("koi_5")]; + tensor qo_3_axis_0 = const()[name = tensor("qo_3_axis_0"), val = tensor(-1)]; + tensor qo_3 = stack(axis = qo_3_axis_0, values = (qor_5, qoi_5))[name = tensor("qo_3")]; + tensor ko_3_axis_0 = const()[name = tensor("ko_3_axis_0"), val = tensor(-1)]; + tensor ko_3 = stack(axis = ko_3_axis_0, values = (kor_5, koi_5))[name = tensor("ko_3")]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1, 16, 64])]; + tensor q_9 = reshape(shape = var_838, x = qo_3)[name = tensor("q_9")]; + tensor var_840 = const()[name = tensor("op_840"), val = tensor([1, 1, 16, 64])]; + tensor k_7 = reshape(shape = var_840, x = ko_3)[name = tensor("k_7")]; + tensor _inversed_862_y_0 = const()[name = tensor("_inversed_862_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_862 = mul(x = ts_11, y = _inversed_862_y_0)[name = tensor("_inversed_862")]; + tensor var_863 = floor(x = _inversed_862)[name = tensor("op_863")]; + tensor var_864 = const()[name = tensor("op_864"), val = tensor(0x1p+9)]; + tensor var_865 = mul(x = var_863, y = var_864)[name = tensor("op_865")]; + tensor write_indices_float_7 = sub(x = ts_11, y = var_865)[name = tensor("write_indices_float_7")]; + tensor var_872_dtype_0 = const()[name = tensor("op_872_dtype_0"), val = tensor("int32")]; + tensor write_indices_3_reps_0 = const()[name = tensor("write_indices_3_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_872 = cast(dtype = var_872_dtype_0, x = write_indices_float_7)[name = tensor("cast_445")]; + tensor write_indices_3 = tile(reps = write_indices_3_reps_0, x = var_872)[name = tensor("write_indices_3")]; + tensor var_880_begin_0 = const()[name = tensor("op_880_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_880_end_0 = const()[name = tensor("op_880_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_880_end_mask_0 = const()[name = tensor("op_880_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_880_squeeze_mask_0 = const()[name = tensor("op_880_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_880 = slice_by_index(begin = var_880_begin_0, end = var_880_end_0, end_mask = var_880_end_mask_0, squeeze_mask = var_880_squeeze_mask_0, x = cache1)[name = tensor("op_880")]; + tensor var_882_axis_0 = const()[name = tensor("op_882_axis_0"), val = tensor(1)]; + tensor var_882_mode_0 = const()[name = tensor("op_882_mode_0"), val = tensor("update")]; + tensor var_882_validate_indices_0 = const()[name = tensor("op_882_validate_indices_0"), val = tensor(false)]; + tensor var_882 = scatter_along_axis(axis = var_882_axis_0, data = var_880, indices = write_indices_3, mode = var_882_mode_0, updates = k_7, validate_indices = var_882_validate_indices_0)[name = tensor("op_882")]; + tensor concat_8 = const()[name = tensor("concat_8"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_9 = const()[name = tensor("concat_9"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_48 = const()[name = tensor("shape_48"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_2 = const()[name = tensor("reduce_prod_2"), val = tensor(1048576)]; + tensor range_1d_2_start_0 = const()[name = tensor("range_1d_2_start_0"), val = tensor(0)]; + tensor range_1d_2_step_0 = const()[name = tensor("range_1d_2_step_0"), val = tensor(1)]; + tensor range_1d_2 = range_1d(end = reduce_prod_2, start = range_1d_2_start_0, step = range_1d_2_step_0)[name = tensor("range_1d_2")]; + tensor reshape_10 = reshape(shape = shape_48, x = range_1d_2)[name = tensor("reshape_10")]; + tensor slice_by_index_2 = slice_by_index(begin = concat_8, begin_mask = new_cache_3_internal_tensor_assign_1_begin_mask_0, end = concat_9, end_mask = new_cache_3_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_1_stride_0, x = reshape_10)[name = tensor("slice_by_index_2")]; + tensor reshape_11_shape_0 = const()[name = tensor("reshape_11_shape_0"), val = tensor([-1])]; + tensor reshape_11 = reshape(shape = reshape_11_shape_0, x = slice_by_index_2)[name = tensor("reshape_11")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([-1])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = var_882)[name = tensor("reshape_12")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([-1])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = cache1)[name = tensor("reshape_13")]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2_validate_indices_0 = const()[name = tensor("scatter_2_validate_indices_0"), val = tensor(false)]; + tensor scatter_2 = scatter(axis = scatter_2_axis_0, data = reshape_13, indices = reshape_11, mode = scatter_2_mode_0, updates = reshape_12, validate_indices = scatter_2_validate_indices_0)[name = tensor("scatter_2")]; + tensor reshape_14 = reshape(shape = shape_48, x = scatter_2)[name = tensor("reshape_14")]; + tensor var_890_begin_0 = const()[name = tensor("op_890_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_890_end_0 = const()[name = tensor("op_890_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_890_end_mask_0 = const()[name = tensor("op_890_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_890_squeeze_mask_0 = const()[name = tensor("op_890_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_890 = slice_by_index(begin = var_890_begin_0, end = var_890_end_0, end_mask = var_890_end_mask_0, squeeze_mask = var_890_squeeze_mask_0, x = reshape_14)[name = tensor("op_890")]; + tensor var_892_axis_0 = const()[name = tensor("op_892_axis_0"), val = tensor(1)]; + tensor var_892_mode_0 = const()[name = tensor("op_892_mode_0"), val = tensor("update")]; + tensor var_892_validate_indices_0 = const()[name = tensor("op_892_validate_indices_0"), val = tensor(false)]; + tensor var_892 = scatter_along_axis(axis = var_892_axis_0, data = var_890, indices = write_indices_3, mode = var_892_mode_0, updates = v_3, validate_indices = var_892_validate_indices_0)[name = tensor("op_892")]; + tensor concat_10 = const()[name = tensor("concat_10"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_11 = const()[name = tensor("concat_11"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_49 = const()[name = tensor("shape_49"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_3 = const()[name = tensor("reduce_prod_3"), val = tensor(1048576)]; + tensor range_1d_3_start_0 = const()[name = tensor("range_1d_3_start_0"), val = tensor(0)]; + tensor range_1d_3_step_0 = const()[name = tensor("range_1d_3_step_0"), val = tensor(1)]; + tensor range_1d_3 = range_1d(end = reduce_prod_3, start = range_1d_3_start_0, step = range_1d_3_step_0)[name = tensor("range_1d_3")]; + tensor reshape_15 = reshape(shape = shape_49, x = range_1d_3)[name = tensor("reshape_15")]; + tensor slice_by_index_3 = slice_by_index(begin = concat_10, begin_mask = new_cache_3_internal_tensor_assign_2_begin_mask_0, end = concat_11, end_mask = new_cache_3_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_2_stride_0, x = reshape_15)[name = tensor("slice_by_index_3")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([-1])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = slice_by_index_3)[name = tensor("reshape_16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([-1])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = var_892)[name = tensor("reshape_17")]; + tensor reshape_18_shape_0 = const()[name = tensor("reshape_18_shape_0"), val = tensor([-1])]; + tensor reshape_18 = reshape(shape = reshape_18_shape_0, x = reshape_14)[name = tensor("reshape_18")]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3_validate_indices_0 = const()[name = tensor("scatter_3_validate_indices_0"), val = tensor(false)]; + tensor scatter_3 = scatter(axis = scatter_3_axis_0, data = reshape_18, indices = reshape_16, mode = scatter_3_mode_0, updates = reshape_17, validate_indices = scatter_3_validate_indices_0)[name = tensor("scatter_3")]; + tensor new_cache_3_internal_tensor_assign_2 = reshape(shape = shape_49, x = scatter_3)[name = tensor("reshape_19")]; + tensor keys_7_begin_0 = const()[name = tensor("keys_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_7_end_0 = const()[name = tensor("keys_7_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_7_end_mask_0 = const()[name = tensor("keys_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_7_squeeze_mask_0 = const()[name = tensor("keys_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_7 = slice_by_index(begin = keys_7_begin_0, end = keys_7_end_0, end_mask = keys_7_end_mask_0, squeeze_mask = keys_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("keys_7")]; + tensor values_7_begin_0 = const()[name = tensor("values_7_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_7_end_0 = const()[name = tensor("values_7_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_7_end_mask_0 = const()[name = tensor("values_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_7_squeeze_mask_0 = const()[name = tensor("values_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_7 = slice_by_index(begin = values_7_begin_0, end = values_7_end_0, end_mask = values_7_end_mask_0, squeeze_mask = values_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("values_7")]; + tensor var_904 = not_equal(x = keys_7, y = keys_7)[name = tensor("op_904")]; + tensor keys_9 = select(a = var_491, b = keys_7, cond = var_904)[name = tensor("keys_9")]; + tensor var_912 = not_equal(x = values_7, y = values_7)[name = tensor("op_912")]; + tensor values_9 = select(a = var_491, b = values_7, cond = var_912)[name = tensor("values_9")]; + tensor var_936 = const()[name = tensor("op_936"), val = tensor([0, 2, 1, 3])]; + tensor var_949 = const()[name = tensor("op_949"), val = tensor([1, 1, 1])]; + tensor var_950 = reshape(shape = var_949, x = position1)[name = tensor("op_950")]; + tensor var_967 = const()[name = tensor("op_967"), val = tensor(0x1p+0)]; + tensor valid_len_3 = add(x = var_950, y = var_967)[name = tensor("valid_len_3")]; + tensor valid_mask_3 = less(x = k_positions_1_promoted, y = valid_len_3)[name = tensor("valid_mask_3")]; + tensor causal_mask_3 = less_equal(x = k_positions_1_promoted, y = var_950)[name = tensor("causal_mask_3")]; + tensor attn_mask_5 = logical_and(x = valid_mask_3, y = causal_mask_3)[name = tensor("attn_mask_5")]; + tensor attn_mask_7_axes_0 = const()[name = tensor("attn_mask_7_axes_0"), val = tensor([1])]; + tensor attn_mask_7 = expand_dims(axes = attn_mask_7_axes_0, x = attn_mask_5)[name = tensor("attn_mask_7")]; + tensor var_979 = const()[name = tensor("op_979"), val = tensor([0x1.fffe5cp-4])]; + tensor var_985_transpose_x_0 = const()[name = tensor("op_985_transpose_x_0"), val = tensor(false)]; + tensor var_985_transpose_y_0 = const()[name = tensor("op_985_transpose_y_0"), val = tensor(false)]; + tensor transpose_71_perm_0 = const()[name = tensor("transpose_71_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = keys_9)[name = tensor("transpose_200")]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = q_9)[name = tensor("transpose_201")]; + tensor var_985 = matmul(transpose_x = var_985_transpose_x_0, transpose_y = var_985_transpose_y_0, x = transpose_71, y = transpose_72)[name = tensor("op_985")]; + tensor attn_weights_7 = mul(x = var_985, y = var_979)[name = tensor("attn_weights_7")]; + tensor var_987 = logical_not(x = attn_mask_7)[name = tensor("op_987")]; + tensor var_988 = const()[name = tensor("op_988"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_9 = select(a = var_988, b = attn_weights_7, cond = var_987)[name = tensor("attn_weights_9")]; + tensor var_990 = const()[name = tensor("op_990"), val = tensor(-1)]; + tensor attn_weights_11 = softmax(axis = var_990, x = attn_weights_9)[name = tensor("attn_weights_11")]; + tensor attn_output_3_transpose_x_0 = const()[name = tensor("attn_output_3_transpose_x_0"), val = tensor(false)]; + tensor attn_output_3_transpose_y_0 = const()[name = tensor("attn_output_3_transpose_y_0"), val = tensor(false)]; + tensor values_11 = transpose(perm = var_936, x = values_9)[name = tensor("transpose_202")]; + tensor attn_output_3 = matmul(transpose_x = attn_output_3_transpose_x_0, transpose_y = attn_output_3_transpose_y_0, x = attn_weights_11, y = values_11)[name = tensor("attn_output_3")]; + tensor var_998 = const()[name = tensor("op_998"), val = tensor([0, 2, 1, 3])]; + tensor var_1001 = const()[name = tensor("op_1001"), val = tensor([1, 1, 1024])]; + tensor var_999 = transpose(perm = var_998, x = attn_output_3)[name = tensor("transpose_199")]; + tensor input_13 = reshape(shape = var_1001, x = var_999)[name = tensor("input_13")]; + tensor attn_out_3 = linear(bias = linear_1_bias_0, weight = attn1_out_proj_weight, x = input_13)[name = tensor("linear_5")]; + tensor var_1007 = const()[name = tensor("op_1007"), val = tensor(0x1p+0)]; + tensor var_1008 = add(x = position1, y = var_1007)[name = tensor("op_1008")]; + tensor input_15 = add(x = input_11, y = attn_out_3)[name = tensor("input_15")]; + tensor var_1012 = const()[name = tensor("op_1012"), val = tensor(0x1.4f8b58p-17)]; + tensor input_17_axes_0 = const()[name = tensor("input_17_axes_0"), val = tensor([-1])]; + tensor input_17 = layer_norm(axes = input_17_axes_0, beta = norm1_2_bias, epsilon = var_1012, gamma = norm1_2_weight, x = input_15)[name = tensor("input_17")]; + tensor var_1020 = linear(bias = linear_2_bias_0, weight = linear1_1_weight, x = input_17)[name = tensor("linear_6")]; + tensor input_19_mode_0 = const()[name = tensor("input_19_mode_0"), val = tensor("EXACT")]; + tensor input_19 = gelu(mode = input_19_mode_0, x = var_1020)[name = tensor("input_19")]; + tensor ffn_out_3 = linear(bias = linear_1_bias_0, weight = linear1_2_weight, x = input_19)[name = tensor("linear_7")]; + tensor input_21 = add(x = input_15, y = ffn_out_3)[name = tensor("input_21")]; + tensor var_1029 = const()[name = tensor("op_1029"), val = tensor(0x1.4f8b58p-17)]; + tensor x_5_axes_0 = const()[name = tensor("x_5_axes_0"), val = tensor([-1])]; + tensor x_5 = layer_norm(axes = x_5_axes_0, beta = norm2_1_bias, epsilon = var_1029, gamma = norm2_1_weight, x = input_21)[name = tensor("x_5")]; + tensor var_1061 = linear(bias = linear_0_bias_0, weight = attn2_in_proj_weight, x = x_5)[name = tensor("linear_8")]; + tensor var_1065 = const()[name = tensor("op_1065"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_5 = reshape(shape = var_1065, x = var_1061)[name = tensor("qkv_5")]; + tensor q_13_begin_0 = const()[name = tensor("q_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_13_end_0 = const()[name = tensor("q_13_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_13_end_mask_0 = const()[name = tensor("q_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_13_squeeze_mask_0 = const()[name = tensor("q_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_13 = slice_by_index(begin = q_13_begin_0, end = q_13_end_0, end_mask = q_13_end_mask_0, squeeze_mask = q_13_squeeze_mask_0, x = qkv_5)[name = tensor("q_13")]; + tensor k_9_begin_0 = const()[name = tensor("k_9_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_9_end_0 = const()[name = tensor("k_9_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_9_end_mask_0 = const()[name = tensor("k_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_9_squeeze_mask_0 = const()[name = tensor("k_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_9 = slice_by_index(begin = k_9_begin_0, end = k_9_end_0, end_mask = k_9_end_mask_0, squeeze_mask = k_9_squeeze_mask_0, x = qkv_5)[name = tensor("k_9")]; + tensor v_5_begin_0 = const()[name = tensor("v_5_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_5_end_0 = const()[name = tensor("v_5_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_5_end_mask_0 = const()[name = tensor("v_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_5_squeeze_mask_0 = const()[name = tensor("v_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_5 = slice_by_index(begin = v_5_begin_0, end = v_5_end_0, end_mask = v_5_end_mask_0, squeeze_mask = v_5_squeeze_mask_0, x = qkv_5)[name = tensor("v_5")]; + tensor freqs_5 = const()[name = tensor("freqs_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172740544)))]; + tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([1, 1, 1, 1])]; + tensor ts_17 = reshape(shape = var_1169, x = position2)[name = tensor("ts_17")]; + tensor var_1173 = const()[name = tensor("op_1173"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_5 = reshape(shape = var_1173, x = q_13)[name = tensor("q_complex_5")]; + tensor var_1177 = const()[name = tensor("op_1177"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_5 = reshape(shape = var_1177, x = k_9)[name = tensor("k_complex_5")]; + tensor var_1181_begin_0 = const()[name = tensor("op_1181_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1181_end_0 = const()[name = tensor("op_1181_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1181_end_mask_0 = const()[name = tensor("op_1181_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1181_squeeze_mask_0 = const()[name = tensor("op_1181_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1181 = slice_by_index(begin = var_1181_begin_0, end = var_1181_end_0, end_mask = var_1181_end_mask_0, squeeze_mask = var_1181_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1181")]; + tensor var_1189_begin_0 = const()[name = tensor("op_1189_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1189_end_0 = const()[name = tensor("op_1189_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1189_end_mask_0 = const()[name = tensor("op_1189_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1189_squeeze_mask_0 = const()[name = tensor("op_1189_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1189 = slice_by_index(begin = var_1189_begin_0, end = var_1189_end_0, end_mask = var_1189_end_mask_0, squeeze_mask = var_1189_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1189")]; + tensor var_1197_begin_0 = const()[name = tensor("op_1197_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1197_end_0 = const()[name = tensor("op_1197_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1197_end_mask_0 = const()[name = tensor("op_1197_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1197_squeeze_mask_0 = const()[name = tensor("op_1197_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1197 = slice_by_index(begin = var_1197_begin_0, end = var_1197_end_0, end_mask = var_1197_end_mask_0, squeeze_mask = var_1197_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1197")]; + tensor var_1205_begin_0 = const()[name = tensor("op_1205_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1205_end_0 = const()[name = tensor("op_1205_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1205_end_mask_0 = const()[name = tensor("op_1205_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1205_squeeze_mask_0 = const()[name = tensor("op_1205_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1205 = slice_by_index(begin = var_1205_begin_0, end = var_1205_end_0, end_mask = var_1205_end_mask_0, squeeze_mask = var_1205_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1205")]; + tensor var_1211 = mul(x = freqs_5, y = ts_17)[name = tensor("op_1211")]; + tensor rotr_5 = cos(x = var_1211)[name = tensor("rotr_5")]; + tensor roti_5 = sin(x = var_1211)[name = tensor("roti_5")]; + tensor var_1215 = mul(x = var_1181, y = rotr_5)[name = tensor("op_1215")]; + tensor var_1216 = mul(x = var_1189, y = roti_5)[name = tensor("op_1216")]; + tensor qor_9 = sub(x = var_1215, y = var_1216)[name = tensor("qor_9")]; + tensor var_1219 = mul(x = var_1181, y = roti_5)[name = tensor("op_1219")]; + tensor var_1220 = mul(x = var_1189, y = rotr_5)[name = tensor("op_1220")]; + tensor qoi_9 = add(x = var_1219, y = var_1220)[name = tensor("qoi_9")]; + tensor var_1223 = mul(x = var_1197, y = rotr_5)[name = tensor("op_1223")]; + tensor var_1224 = mul(x = var_1205, y = roti_5)[name = tensor("op_1224")]; + tensor kor_9 = sub(x = var_1223, y = var_1224)[name = tensor("kor_9")]; + tensor var_1227 = mul(x = var_1197, y = roti_5)[name = tensor("op_1227")]; + tensor var_1228 = mul(x = var_1205, y = rotr_5)[name = tensor("op_1228")]; + tensor koi_9 = add(x = var_1227, y = var_1228)[name = tensor("koi_9")]; + tensor qo_5_axis_0 = const()[name = tensor("qo_5_axis_0"), val = tensor(-1)]; + tensor qo_5 = stack(axis = qo_5_axis_0, values = (qor_9, qoi_9))[name = tensor("qo_5")]; + tensor ko_5_axis_0 = const()[name = tensor("ko_5_axis_0"), val = tensor(-1)]; + tensor ko_5 = stack(axis = ko_5_axis_0, values = (kor_9, koi_9))[name = tensor("ko_5")]; + tensor var_1257 = const()[name = tensor("op_1257"), val = tensor([1, 1, 16, 64])]; + tensor q_15 = reshape(shape = var_1257, x = qo_5)[name = tensor("q_15")]; + tensor var_1259 = const()[name = tensor("op_1259"), val = tensor([1, 1, 16, 64])]; + tensor k_11 = reshape(shape = var_1259, x = ko_5)[name = tensor("k_11")]; + tensor _inversed_1281_y_0 = const()[name = tensor("_inversed_1281_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1281 = mul(x = ts_17, y = _inversed_1281_y_0)[name = tensor("_inversed_1281")]; + tensor var_1282 = floor(x = _inversed_1281)[name = tensor("op_1282")]; + tensor var_1283 = const()[name = tensor("op_1283"), val = tensor(0x1p+9)]; + tensor var_1284 = mul(x = var_1282, y = var_1283)[name = tensor("op_1284")]; + tensor write_indices_float_11 = sub(x = ts_17, y = var_1284)[name = tensor("write_indices_float_11")]; + tensor var_1291_dtype_0 = const()[name = tensor("op_1291_dtype_0"), val = tensor("int32")]; + tensor write_indices_5_reps_0 = const()[name = tensor("write_indices_5_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1291 = cast(dtype = var_1291_dtype_0, x = write_indices_float_11)[name = tensor("cast_444")]; + tensor write_indices_5 = tile(reps = write_indices_5_reps_0, x = var_1291)[name = tensor("write_indices_5")]; + tensor var_1299_begin_0 = const()[name = tensor("op_1299_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1299_end_0 = const()[name = tensor("op_1299_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1299_end_mask_0 = const()[name = tensor("op_1299_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1299_squeeze_mask_0 = const()[name = tensor("op_1299_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1299 = slice_by_index(begin = var_1299_begin_0, end = var_1299_end_0, end_mask = var_1299_end_mask_0, squeeze_mask = var_1299_squeeze_mask_0, x = cache2)[name = tensor("op_1299")]; + tensor var_1301_axis_0 = const()[name = tensor("op_1301_axis_0"), val = tensor(1)]; + tensor var_1301_mode_0 = const()[name = tensor("op_1301_mode_0"), val = tensor("update")]; + tensor var_1301_validate_indices_0 = const()[name = tensor("op_1301_validate_indices_0"), val = tensor(false)]; + tensor var_1301 = scatter_along_axis(axis = var_1301_axis_0, data = var_1299, indices = write_indices_5, mode = var_1301_mode_0, updates = k_11, validate_indices = var_1301_validate_indices_0)[name = tensor("op_1301")]; + tensor concat_15 = const()[name = tensor("concat_15"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_16 = const()[name = tensor("concat_16"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_50 = const()[name = tensor("shape_50"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_4 = const()[name = tensor("reduce_prod_4"), val = tensor(1048576)]; + tensor range_1d_4_start_0 = const()[name = tensor("range_1d_4_start_0"), val = tensor(0)]; + tensor range_1d_4_step_0 = const()[name = tensor("range_1d_4_step_0"), val = tensor(1)]; + tensor range_1d_4 = range_1d(end = reduce_prod_4, start = range_1d_4_start_0, step = range_1d_4_step_0)[name = tensor("range_1d_4")]; + tensor reshape_20 = reshape(shape = shape_50, x = range_1d_4)[name = tensor("reshape_20")]; + tensor slice_by_index_4 = slice_by_index(begin = concat_15, begin_mask = new_cache_5_internal_tensor_assign_1_begin_mask_0, end = concat_16, end_mask = new_cache_5_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_1_stride_0, x = reshape_20)[name = tensor("slice_by_index_4")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([-1])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = slice_by_index_4)[name = tensor("reshape_21")]; + tensor reshape_22_shape_0 = const()[name = tensor("reshape_22_shape_0"), val = tensor([-1])]; + tensor reshape_22 = reshape(shape = reshape_22_shape_0, x = var_1301)[name = tensor("reshape_22")]; + tensor reshape_23_shape_0 = const()[name = tensor("reshape_23_shape_0"), val = tensor([-1])]; + tensor reshape_23 = reshape(shape = reshape_23_shape_0, x = cache2)[name = tensor("reshape_23")]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4_validate_indices_0 = const()[name = tensor("scatter_4_validate_indices_0"), val = tensor(false)]; + tensor scatter_4 = scatter(axis = scatter_4_axis_0, data = reshape_23, indices = reshape_21, mode = scatter_4_mode_0, updates = reshape_22, validate_indices = scatter_4_validate_indices_0)[name = tensor("scatter_4")]; + tensor reshape_24 = reshape(shape = shape_50, x = scatter_4)[name = tensor("reshape_24")]; + tensor var_1309_begin_0 = const()[name = tensor("op_1309_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1309_end_0 = const()[name = tensor("op_1309_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1309_end_mask_0 = const()[name = tensor("op_1309_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1309_squeeze_mask_0 = const()[name = tensor("op_1309_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1309 = slice_by_index(begin = var_1309_begin_0, end = var_1309_end_0, end_mask = var_1309_end_mask_0, squeeze_mask = var_1309_squeeze_mask_0, x = reshape_24)[name = tensor("op_1309")]; + tensor var_1311_axis_0 = const()[name = tensor("op_1311_axis_0"), val = tensor(1)]; + tensor var_1311_mode_0 = const()[name = tensor("op_1311_mode_0"), val = tensor("update")]; + tensor var_1311_validate_indices_0 = const()[name = tensor("op_1311_validate_indices_0"), val = tensor(false)]; + tensor var_1311 = scatter_along_axis(axis = var_1311_axis_0, data = var_1309, indices = write_indices_5, mode = var_1311_mode_0, updates = v_5, validate_indices = var_1311_validate_indices_0)[name = tensor("op_1311")]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_18 = const()[name = tensor("concat_18"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_51 = const()[name = tensor("shape_51"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_5 = const()[name = tensor("reduce_prod_5"), val = tensor(1048576)]; + tensor range_1d_5_start_0 = const()[name = tensor("range_1d_5_start_0"), val = tensor(0)]; + tensor range_1d_5_step_0 = const()[name = tensor("range_1d_5_step_0"), val = tensor(1)]; + tensor range_1d_5 = range_1d(end = reduce_prod_5, start = range_1d_5_start_0, step = range_1d_5_step_0)[name = tensor("range_1d_5")]; + tensor reshape_25 = reshape(shape = shape_51, x = range_1d_5)[name = tensor("reshape_25")]; + tensor slice_by_index_5 = slice_by_index(begin = concat_17, begin_mask = new_cache_5_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = new_cache_5_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_2_stride_0, x = reshape_25)[name = tensor("slice_by_index_5")]; + tensor reshape_26_shape_0 = const()[name = tensor("reshape_26_shape_0"), val = tensor([-1])]; + tensor reshape_26 = reshape(shape = reshape_26_shape_0, x = slice_by_index_5)[name = tensor("reshape_26")]; + tensor reshape_27_shape_0 = const()[name = tensor("reshape_27_shape_0"), val = tensor([-1])]; + tensor reshape_27 = reshape(shape = reshape_27_shape_0, x = var_1311)[name = tensor("reshape_27")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([-1])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = reshape_24)[name = tensor("reshape_28")]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5_validate_indices_0 = const()[name = tensor("scatter_5_validate_indices_0"), val = tensor(false)]; + tensor scatter_5 = scatter(axis = scatter_5_axis_0, data = reshape_28, indices = reshape_26, mode = scatter_5_mode_0, updates = reshape_27, validate_indices = scatter_5_validate_indices_0)[name = tensor("scatter_5")]; + tensor new_cache_5_internal_tensor_assign_2 = reshape(shape = shape_51, x = scatter_5)[name = tensor("reshape_29")]; + tensor keys_13_begin_0 = const()[name = tensor("keys_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_13_end_0 = const()[name = tensor("keys_13_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_13_end_mask_0 = const()[name = tensor("keys_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_13_squeeze_mask_0 = const()[name = tensor("keys_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_13 = slice_by_index(begin = keys_13_begin_0, end = keys_13_end_0, end_mask = keys_13_end_mask_0, squeeze_mask = keys_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("keys_13")]; + tensor values_13_begin_0 = const()[name = tensor("values_13_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_13_end_0 = const()[name = tensor("values_13_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_13_end_mask_0 = const()[name = tensor("values_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_13_squeeze_mask_0 = const()[name = tensor("values_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_13 = slice_by_index(begin = values_13_begin_0, end = values_13_end_0, end_mask = values_13_end_mask_0, squeeze_mask = values_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("values_13")]; + tensor var_1323 = not_equal(x = keys_13, y = keys_13)[name = tensor("op_1323")]; + tensor keys_15 = select(a = var_491, b = keys_13, cond = var_1323)[name = tensor("keys_15")]; + tensor var_1331 = not_equal(x = values_13, y = values_13)[name = tensor("op_1331")]; + tensor values_15 = select(a = var_491, b = values_13, cond = var_1331)[name = tensor("values_15")]; + tensor var_1355 = const()[name = tensor("op_1355"), val = tensor([0, 2, 1, 3])]; + tensor var_1368 = const()[name = tensor("op_1368"), val = tensor([1, 1, 1])]; + tensor var_1369 = reshape(shape = var_1368, x = position2)[name = tensor("op_1369")]; + tensor var_1386 = const()[name = tensor("op_1386"), val = tensor(0x1p+0)]; + tensor valid_len_5 = add(x = var_1369, y = var_1386)[name = tensor("valid_len_5")]; + tensor valid_mask_5 = less(x = k_positions_1_promoted, y = valid_len_5)[name = tensor("valid_mask_5")]; + tensor causal_mask_5 = less_equal(x = k_positions_1_promoted, y = var_1369)[name = tensor("causal_mask_5")]; + tensor attn_mask_9 = logical_and(x = valid_mask_5, y = causal_mask_5)[name = tensor("attn_mask_9")]; + tensor attn_mask_11_axes_0 = const()[name = tensor("attn_mask_11_axes_0"), val = tensor([1])]; + tensor attn_mask_11 = expand_dims(axes = attn_mask_11_axes_0, x = attn_mask_9)[name = tensor("attn_mask_11")]; + tensor var_1398 = const()[name = tensor("op_1398"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1404_transpose_x_0 = const()[name = tensor("op_1404_transpose_x_0"), val = tensor(false)]; + tensor var_1404_transpose_y_0 = const()[name = tensor("op_1404_transpose_y_0"), val = tensor(false)]; + tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = keys_15)[name = tensor("transpose_196")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = q_15)[name = tensor("transpose_197")]; + tensor var_1404 = matmul(transpose_x = var_1404_transpose_x_0, transpose_y = var_1404_transpose_y_0, x = transpose_73, y = transpose_74)[name = tensor("op_1404")]; + tensor attn_weights_13 = mul(x = var_1404, y = var_1398)[name = tensor("attn_weights_13")]; + tensor var_1406 = logical_not(x = attn_mask_11)[name = tensor("op_1406")]; + tensor var_1407 = const()[name = tensor("op_1407"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_15 = select(a = var_1407, b = attn_weights_13, cond = var_1406)[name = tensor("attn_weights_15")]; + tensor var_1409 = const()[name = tensor("op_1409"), val = tensor(-1)]; + tensor attn_weights_17 = softmax(axis = var_1409, x = attn_weights_15)[name = tensor("attn_weights_17")]; + tensor attn_output_5_transpose_x_0 = const()[name = tensor("attn_output_5_transpose_x_0"), val = tensor(false)]; + tensor attn_output_5_transpose_y_0 = const()[name = tensor("attn_output_5_transpose_y_0"), val = tensor(false)]; + tensor values_17 = transpose(perm = var_1355, x = values_15)[name = tensor("transpose_198")]; + tensor attn_output_5 = matmul(transpose_x = attn_output_5_transpose_x_0, transpose_y = attn_output_5_transpose_y_0, x = attn_weights_17, y = values_17)[name = tensor("attn_output_5")]; + tensor var_1417 = const()[name = tensor("op_1417"), val = tensor([0, 2, 1, 3])]; + tensor var_1420 = const()[name = tensor("op_1420"), val = tensor([1, 1, 1024])]; + tensor var_1418 = transpose(perm = var_1417, x = attn_output_5)[name = tensor("transpose_195")]; + tensor input_23 = reshape(shape = var_1420, x = var_1418)[name = tensor("input_23")]; + tensor attn_out_5 = linear(bias = linear_1_bias_0, weight = attn2_out_proj_weight, x = input_23)[name = tensor("linear_9")]; + tensor var_1426 = const()[name = tensor("op_1426"), val = tensor(0x1p+0)]; + tensor var_1427 = add(x = position2, y = var_1426)[name = tensor("op_1427")]; + tensor input_25 = add(x = input_21, y = attn_out_5)[name = tensor("input_25")]; + tensor var_1431 = const()[name = tensor("op_1431"), val = tensor(0x1.4f8b58p-17)]; + tensor input_27_axes_0 = const()[name = tensor("input_27_axes_0"), val = tensor([-1])]; + tensor input_27 = layer_norm(axes = input_27_axes_0, beta = norm2_2_bias, epsilon = var_1431, gamma = norm2_2_weight, x = input_25)[name = tensor("input_27")]; + tensor var_1439 = linear(bias = linear_2_bias_0, weight = linear2_1_weight, x = input_27)[name = tensor("linear_10")]; + tensor input_29_mode_0 = const()[name = tensor("input_29_mode_0"), val = tensor("EXACT")]; + tensor input_29 = gelu(mode = input_29_mode_0, x = var_1439)[name = tensor("input_29")]; + tensor ffn_out_5 = linear(bias = linear_1_bias_0, weight = linear2_2_weight, x = input_29)[name = tensor("linear_11")]; + tensor input_31 = add(x = input_25, y = ffn_out_5)[name = tensor("input_31")]; + tensor var_1448 = const()[name = tensor("op_1448"), val = tensor(0x1.4f8b58p-17)]; + tensor x_7_axes_0 = const()[name = tensor("x_7_axes_0"), val = tensor([-1])]; + tensor x_7 = layer_norm(axes = x_7_axes_0, beta = norm3_1_bias, epsilon = var_1448, gamma = norm3_1_weight, x = input_31)[name = tensor("x_7")]; + tensor var_1480 = linear(bias = linear_0_bias_0, weight = attn3_in_proj_weight, x = x_7)[name = tensor("linear_12")]; + tensor var_1484 = const()[name = tensor("op_1484"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_7 = reshape(shape = var_1484, x = var_1480)[name = tensor("qkv_7")]; + tensor q_19_begin_0 = const()[name = tensor("q_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_19_end_0 = const()[name = tensor("q_19_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_19_end_mask_0 = const()[name = tensor("q_19_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_19_squeeze_mask_0 = const()[name = tensor("q_19_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_19 = slice_by_index(begin = q_19_begin_0, end = q_19_end_0, end_mask = q_19_end_mask_0, squeeze_mask = q_19_squeeze_mask_0, x = qkv_7)[name = tensor("q_19")]; + tensor k_13_begin_0 = const()[name = tensor("k_13_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_13_end_0 = const()[name = tensor("k_13_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_13_end_mask_0 = const()[name = tensor("k_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_13_squeeze_mask_0 = const()[name = tensor("k_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_13 = slice_by_index(begin = k_13_begin_0, end = k_13_end_0, end_mask = k_13_end_mask_0, squeeze_mask = k_13_squeeze_mask_0, x = qkv_7)[name = tensor("k_13")]; + tensor v_7_begin_0 = const()[name = tensor("v_7_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_7_end_0 = const()[name = tensor("v_7_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_7_end_mask_0 = const()[name = tensor("v_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_7_squeeze_mask_0 = const()[name = tensor("v_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_7 = slice_by_index(begin = v_7_begin_0, end = v_7_end_0, end_mask = v_7_end_mask_0, squeeze_mask = v_7_squeeze_mask_0, x = qkv_7)[name = tensor("v_7")]; + tensor freqs_7 = const()[name = tensor("freqs_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172740736)))]; + tensor var_1588 = const()[name = tensor("op_1588"), val = tensor([1, 1, 1, 1])]; + tensor ts_23 = reshape(shape = var_1588, x = position3)[name = tensor("ts_23")]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_7 = reshape(shape = var_1592, x = q_19)[name = tensor("q_complex_7")]; + tensor var_1596 = const()[name = tensor("op_1596"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_7 = reshape(shape = var_1596, x = k_13)[name = tensor("k_complex_7")]; + tensor var_1600_begin_0 = const()[name = tensor("op_1600_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1600_end_0 = const()[name = tensor("op_1600_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1600_end_mask_0 = const()[name = tensor("op_1600_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1600_squeeze_mask_0 = const()[name = tensor("op_1600_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1600 = slice_by_index(begin = var_1600_begin_0, end = var_1600_end_0, end_mask = var_1600_end_mask_0, squeeze_mask = var_1600_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1600")]; + tensor var_1608_begin_0 = const()[name = tensor("op_1608_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1608_end_0 = const()[name = tensor("op_1608_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1608_end_mask_0 = const()[name = tensor("op_1608_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1608_squeeze_mask_0 = const()[name = tensor("op_1608_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1608 = slice_by_index(begin = var_1608_begin_0, end = var_1608_end_0, end_mask = var_1608_end_mask_0, squeeze_mask = var_1608_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1608")]; + tensor var_1616_begin_0 = const()[name = tensor("op_1616_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1616_end_0 = const()[name = tensor("op_1616_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1616_end_mask_0 = const()[name = tensor("op_1616_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1616_squeeze_mask_0 = const()[name = tensor("op_1616_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1616 = slice_by_index(begin = var_1616_begin_0, end = var_1616_end_0, end_mask = var_1616_end_mask_0, squeeze_mask = var_1616_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1616")]; + tensor var_1624_begin_0 = const()[name = tensor("op_1624_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1624_end_0 = const()[name = tensor("op_1624_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1624_end_mask_0 = const()[name = tensor("op_1624_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1624_squeeze_mask_0 = const()[name = tensor("op_1624_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1624 = slice_by_index(begin = var_1624_begin_0, end = var_1624_end_0, end_mask = var_1624_end_mask_0, squeeze_mask = var_1624_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1624")]; + tensor var_1630 = mul(x = freqs_7, y = ts_23)[name = tensor("op_1630")]; + tensor rotr_7 = cos(x = var_1630)[name = tensor("rotr_7")]; + tensor roti_7 = sin(x = var_1630)[name = tensor("roti_7")]; + tensor var_1634 = mul(x = var_1600, y = rotr_7)[name = tensor("op_1634")]; + tensor var_1635 = mul(x = var_1608, y = roti_7)[name = tensor("op_1635")]; + tensor qor_13 = sub(x = var_1634, y = var_1635)[name = tensor("qor_13")]; + tensor var_1638 = mul(x = var_1600, y = roti_7)[name = tensor("op_1638")]; + tensor var_1639 = mul(x = var_1608, y = rotr_7)[name = tensor("op_1639")]; + tensor qoi_13 = add(x = var_1638, y = var_1639)[name = tensor("qoi_13")]; + tensor var_1642 = mul(x = var_1616, y = rotr_7)[name = tensor("op_1642")]; + tensor var_1643 = mul(x = var_1624, y = roti_7)[name = tensor("op_1643")]; + tensor kor_13 = sub(x = var_1642, y = var_1643)[name = tensor("kor_13")]; + tensor var_1646 = mul(x = var_1616, y = roti_7)[name = tensor("op_1646")]; + tensor var_1647 = mul(x = var_1624, y = rotr_7)[name = tensor("op_1647")]; + tensor koi_13 = add(x = var_1646, y = var_1647)[name = tensor("koi_13")]; + tensor qo_7_axis_0 = const()[name = tensor("qo_7_axis_0"), val = tensor(-1)]; + tensor qo_7 = stack(axis = qo_7_axis_0, values = (qor_13, qoi_13))[name = tensor("qo_7")]; + tensor ko_7_axis_0 = const()[name = tensor("ko_7_axis_0"), val = tensor(-1)]; + tensor ko_7 = stack(axis = ko_7_axis_0, values = (kor_13, koi_13))[name = tensor("ko_7")]; + tensor var_1676 = const()[name = tensor("op_1676"), val = tensor([1, 1, 16, 64])]; + tensor q_21 = reshape(shape = var_1676, x = qo_7)[name = tensor("q_21")]; + tensor var_1678 = const()[name = tensor("op_1678"), val = tensor([1, 1, 16, 64])]; + tensor k_15 = reshape(shape = var_1678, x = ko_7)[name = tensor("k_15")]; + tensor _inversed_1700_y_0 = const()[name = tensor("_inversed_1700_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1700 = mul(x = ts_23, y = _inversed_1700_y_0)[name = tensor("_inversed_1700")]; + tensor var_1701 = floor(x = _inversed_1700)[name = tensor("op_1701")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor(0x1p+9)]; + tensor var_1703 = mul(x = var_1701, y = var_1702)[name = tensor("op_1703")]; + tensor write_indices_float_15 = sub(x = ts_23, y = var_1703)[name = tensor("write_indices_float_15")]; + tensor var_1710_dtype_0 = const()[name = tensor("op_1710_dtype_0"), val = tensor("int32")]; + tensor write_indices_7_reps_0 = const()[name = tensor("write_indices_7_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1710 = cast(dtype = var_1710_dtype_0, x = write_indices_float_15)[name = tensor("cast_443")]; + tensor write_indices_7 = tile(reps = write_indices_7_reps_0, x = var_1710)[name = tensor("write_indices_7")]; + tensor var_1718_begin_0 = const()[name = tensor("op_1718_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1718_end_0 = const()[name = tensor("op_1718_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1718_end_mask_0 = const()[name = tensor("op_1718_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1718_squeeze_mask_0 = const()[name = tensor("op_1718_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1718 = slice_by_index(begin = var_1718_begin_0, end = var_1718_end_0, end_mask = var_1718_end_mask_0, squeeze_mask = var_1718_squeeze_mask_0, x = cache3)[name = tensor("op_1718")]; + tensor var_1720_axis_0 = const()[name = tensor("op_1720_axis_0"), val = tensor(1)]; + tensor var_1720_mode_0 = const()[name = tensor("op_1720_mode_0"), val = tensor("update")]; + tensor var_1720_validate_indices_0 = const()[name = tensor("op_1720_validate_indices_0"), val = tensor(false)]; + tensor var_1720 = scatter_along_axis(axis = var_1720_axis_0, data = var_1718, indices = write_indices_7, mode = var_1720_mode_0, updates = k_15, validate_indices = var_1720_validate_indices_0)[name = tensor("op_1720")]; + tensor concat_22 = const()[name = tensor("concat_22"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_23 = const()[name = tensor("concat_23"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_52 = const()[name = tensor("shape_52"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_6 = const()[name = tensor("reduce_prod_6"), val = tensor(1048576)]; + tensor range_1d_6_start_0 = const()[name = tensor("range_1d_6_start_0"), val = tensor(0)]; + tensor range_1d_6_step_0 = const()[name = tensor("range_1d_6_step_0"), val = tensor(1)]; + tensor range_1d_6 = range_1d(end = reduce_prod_6, start = range_1d_6_start_0, step = range_1d_6_step_0)[name = tensor("range_1d_6")]; + tensor reshape_30 = reshape(shape = shape_52, x = range_1d_6)[name = tensor("reshape_30")]; + tensor slice_by_index_6 = slice_by_index(begin = concat_22, begin_mask = new_cache_7_internal_tensor_assign_1_begin_mask_0, end = concat_23, end_mask = new_cache_7_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_1_stride_0, x = reshape_30)[name = tensor("slice_by_index_6")]; + tensor reshape_31_shape_0 = const()[name = tensor("reshape_31_shape_0"), val = tensor([-1])]; + tensor reshape_31 = reshape(shape = reshape_31_shape_0, x = slice_by_index_6)[name = tensor("reshape_31")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([-1])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = var_1720)[name = tensor("reshape_32")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([-1])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = cache3)[name = tensor("reshape_33")]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6_validate_indices_0 = const()[name = tensor("scatter_6_validate_indices_0"), val = tensor(false)]; + tensor scatter_6 = scatter(axis = scatter_6_axis_0, data = reshape_33, indices = reshape_31, mode = scatter_6_mode_0, updates = reshape_32, validate_indices = scatter_6_validate_indices_0)[name = tensor("scatter_6")]; + tensor reshape_34 = reshape(shape = shape_52, x = scatter_6)[name = tensor("reshape_34")]; + tensor var_1728_begin_0 = const()[name = tensor("op_1728_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1728_end_0 = const()[name = tensor("op_1728_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1728_end_mask_0 = const()[name = tensor("op_1728_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1728_squeeze_mask_0 = const()[name = tensor("op_1728_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1728 = slice_by_index(begin = var_1728_begin_0, end = var_1728_end_0, end_mask = var_1728_end_mask_0, squeeze_mask = var_1728_squeeze_mask_0, x = reshape_34)[name = tensor("op_1728")]; + tensor var_1730_axis_0 = const()[name = tensor("op_1730_axis_0"), val = tensor(1)]; + tensor var_1730_mode_0 = const()[name = tensor("op_1730_mode_0"), val = tensor("update")]; + tensor var_1730_validate_indices_0 = const()[name = tensor("op_1730_validate_indices_0"), val = tensor(false)]; + tensor var_1730 = scatter_along_axis(axis = var_1730_axis_0, data = var_1728, indices = write_indices_7, mode = var_1730_mode_0, updates = v_7, validate_indices = var_1730_validate_indices_0)[name = tensor("op_1730")]; + tensor concat_24 = const()[name = tensor("concat_24"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_25 = const()[name = tensor("concat_25"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_53 = const()[name = tensor("shape_53"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_7 = const()[name = tensor("reduce_prod_7"), val = tensor(1048576)]; + tensor range_1d_7_start_0 = const()[name = tensor("range_1d_7_start_0"), val = tensor(0)]; + tensor range_1d_7_step_0 = const()[name = tensor("range_1d_7_step_0"), val = tensor(1)]; + tensor range_1d_7 = range_1d(end = reduce_prod_7, start = range_1d_7_start_0, step = range_1d_7_step_0)[name = tensor("range_1d_7")]; + tensor reshape_35 = reshape(shape = shape_53, x = range_1d_7)[name = tensor("reshape_35")]; + tensor slice_by_index_7 = slice_by_index(begin = concat_24, begin_mask = new_cache_7_internal_tensor_assign_2_begin_mask_0, end = concat_25, end_mask = new_cache_7_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_2_stride_0, x = reshape_35)[name = tensor("slice_by_index_7")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([-1])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = slice_by_index_7)[name = tensor("reshape_36")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([-1])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = var_1730)[name = tensor("reshape_37")]; + tensor reshape_38_shape_0 = const()[name = tensor("reshape_38_shape_0"), val = tensor([-1])]; + tensor reshape_38 = reshape(shape = reshape_38_shape_0, x = reshape_34)[name = tensor("reshape_38")]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7_validate_indices_0 = const()[name = tensor("scatter_7_validate_indices_0"), val = tensor(false)]; + tensor scatter_7 = scatter(axis = scatter_7_axis_0, data = reshape_38, indices = reshape_36, mode = scatter_7_mode_0, updates = reshape_37, validate_indices = scatter_7_validate_indices_0)[name = tensor("scatter_7")]; + tensor new_cache_7_internal_tensor_assign_2 = reshape(shape = shape_53, x = scatter_7)[name = tensor("reshape_39")]; + tensor keys_19_begin_0 = const()[name = tensor("keys_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_19_end_0 = const()[name = tensor("keys_19_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_19_end_mask_0 = const()[name = tensor("keys_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_19_squeeze_mask_0 = const()[name = tensor("keys_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_19 = slice_by_index(begin = keys_19_begin_0, end = keys_19_end_0, end_mask = keys_19_end_mask_0, squeeze_mask = keys_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("keys_19")]; + tensor values_19_begin_0 = const()[name = tensor("values_19_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_19_end_0 = const()[name = tensor("values_19_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_19_end_mask_0 = const()[name = tensor("values_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_19_squeeze_mask_0 = const()[name = tensor("values_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_19 = slice_by_index(begin = values_19_begin_0, end = values_19_end_0, end_mask = values_19_end_mask_0, squeeze_mask = values_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("values_19")]; + tensor var_1742 = not_equal(x = keys_19, y = keys_19)[name = tensor("op_1742")]; + tensor keys_21 = select(a = var_491, b = keys_19, cond = var_1742)[name = tensor("keys_21")]; + tensor var_1750 = not_equal(x = values_19, y = values_19)[name = tensor("op_1750")]; + tensor values_21 = select(a = var_491, b = values_19, cond = var_1750)[name = tensor("values_21")]; + tensor var_1774 = const()[name = tensor("op_1774"), val = tensor([0, 2, 1, 3])]; + tensor var_1787 = const()[name = tensor("op_1787"), val = tensor([1, 1, 1])]; + tensor var_1788 = reshape(shape = var_1787, x = position3)[name = tensor("op_1788")]; + tensor var_1805 = const()[name = tensor("op_1805"), val = tensor(0x1p+0)]; + tensor valid_len_7 = add(x = var_1788, y = var_1805)[name = tensor("valid_len_7")]; + tensor valid_mask_7 = less(x = k_positions_1_promoted, y = valid_len_7)[name = tensor("valid_mask_7")]; + tensor causal_mask_7 = less_equal(x = k_positions_1_promoted, y = var_1788)[name = tensor("causal_mask_7")]; + tensor attn_mask_13 = logical_and(x = valid_mask_7, y = causal_mask_7)[name = tensor("attn_mask_13")]; + tensor attn_mask_15_axes_0 = const()[name = tensor("attn_mask_15_axes_0"), val = tensor([1])]; + tensor attn_mask_15 = expand_dims(axes = attn_mask_15_axes_0, x = attn_mask_13)[name = tensor("attn_mask_15")]; + tensor var_1817 = const()[name = tensor("op_1817"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1823_transpose_x_0 = const()[name = tensor("op_1823_transpose_x_0"), val = tensor(false)]; + tensor var_1823_transpose_y_0 = const()[name = tensor("op_1823_transpose_y_0"), val = tensor(false)]; + tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = keys_21)[name = tensor("transpose_192")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = q_21)[name = tensor("transpose_193")]; + tensor var_1823 = matmul(transpose_x = var_1823_transpose_x_0, transpose_y = var_1823_transpose_y_0, x = transpose_75, y = transpose_76)[name = tensor("op_1823")]; + tensor attn_weights_19 = mul(x = var_1823, y = var_1817)[name = tensor("attn_weights_19")]; + tensor var_1825 = logical_not(x = attn_mask_15)[name = tensor("op_1825")]; + tensor var_1826 = const()[name = tensor("op_1826"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_21 = select(a = var_1826, b = attn_weights_19, cond = var_1825)[name = tensor("attn_weights_21")]; + tensor var_1828 = const()[name = tensor("op_1828"), val = tensor(-1)]; + tensor attn_weights_23 = softmax(axis = var_1828, x = attn_weights_21)[name = tensor("attn_weights_23")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor values_23 = transpose(perm = var_1774, x = values_21)[name = tensor("transpose_194")]; + tensor attn_output_7 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = attn_weights_23, y = values_23)[name = tensor("attn_output_7")]; + tensor var_1836 = const()[name = tensor("op_1836"), val = tensor([0, 2, 1, 3])]; + tensor var_1839 = const()[name = tensor("op_1839"), val = tensor([1, 1, 1024])]; + tensor var_1837 = transpose(perm = var_1836, x = attn_output_7)[name = tensor("transpose_191")]; + tensor input_33 = reshape(shape = var_1839, x = var_1837)[name = tensor("input_33")]; + tensor attn_out_7 = linear(bias = linear_1_bias_0, weight = attn3_out_proj_weight, x = input_33)[name = tensor("linear_13")]; + tensor var_1845 = const()[name = tensor("op_1845"), val = tensor(0x1p+0)]; + tensor var_1846 = add(x = position3, y = var_1845)[name = tensor("op_1846")]; + tensor input_35 = add(x = input_31, y = attn_out_7)[name = tensor("input_35")]; + tensor var_1850 = const()[name = tensor("op_1850"), val = tensor(0x1.4f8b58p-17)]; + tensor input_37_axes_0 = const()[name = tensor("input_37_axes_0"), val = tensor([-1])]; + tensor input_37 = layer_norm(axes = input_37_axes_0, beta = norm3_2_bias, epsilon = var_1850, gamma = norm3_2_weight, x = input_35)[name = tensor("input_37")]; + tensor var_1858 = linear(bias = linear_2_bias_0, weight = linear3_1_weight, x = input_37)[name = tensor("linear_14")]; + tensor input_39_mode_0 = const()[name = tensor("input_39_mode_0"), val = tensor("EXACT")]; + tensor input_39 = gelu(mode = input_39_mode_0, x = var_1858)[name = tensor("input_39")]; + tensor ffn_out_7 = linear(bias = linear_1_bias_0, weight = linear3_2_weight, x = input_39)[name = tensor("linear_15")]; + tensor input_41 = add(x = input_35, y = ffn_out_7)[name = tensor("input_41")]; + tensor var_1867 = const()[name = tensor("op_1867"), val = tensor(0x1.4f8b58p-17)]; + tensor x_9_axes_0 = const()[name = tensor("x_9_axes_0"), val = tensor([-1])]; + tensor x_9 = layer_norm(axes = x_9_axes_0, beta = norm4_1_bias, epsilon = var_1867, gamma = norm4_1_weight, x = input_41)[name = tensor("x_9")]; + tensor var_1899 = linear(bias = linear_0_bias_0, weight = attn4_in_proj_weight, x = x_9)[name = tensor("linear_16")]; + tensor var_1903 = const()[name = tensor("op_1903"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_9 = reshape(shape = var_1903, x = var_1899)[name = tensor("qkv_9")]; + tensor q_25_begin_0 = const()[name = tensor("q_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_25_end_0 = const()[name = tensor("q_25_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_25_end_mask_0 = const()[name = tensor("q_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_25_squeeze_mask_0 = const()[name = tensor("q_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_25 = slice_by_index(begin = q_25_begin_0, end = q_25_end_0, end_mask = q_25_end_mask_0, squeeze_mask = q_25_squeeze_mask_0, x = qkv_9)[name = tensor("q_25")]; + tensor k_17_begin_0 = const()[name = tensor("k_17_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_17_end_0 = const()[name = tensor("k_17_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_17_end_mask_0 = const()[name = tensor("k_17_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_17_squeeze_mask_0 = const()[name = tensor("k_17_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_17 = slice_by_index(begin = k_17_begin_0, end = k_17_end_0, end_mask = k_17_end_mask_0, squeeze_mask = k_17_squeeze_mask_0, x = qkv_9)[name = tensor("k_17")]; + tensor v_9_begin_0 = const()[name = tensor("v_9_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_9_end_0 = const()[name = tensor("v_9_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_9_end_mask_0 = const()[name = tensor("v_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_9_squeeze_mask_0 = const()[name = tensor("v_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_9 = slice_by_index(begin = v_9_begin_0, end = v_9_end_0, end_mask = v_9_end_mask_0, squeeze_mask = v_9_squeeze_mask_0, x = qkv_9)[name = tensor("v_9")]; + tensor freqs_9 = const()[name = tensor("freqs_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172740928)))]; + tensor var_2007 = const()[name = tensor("op_2007"), val = tensor([1, 1, 1, 1])]; + tensor ts_29 = reshape(shape = var_2007, x = position4)[name = tensor("ts_29")]; + tensor var_2011 = const()[name = tensor("op_2011"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_9 = reshape(shape = var_2011, x = q_25)[name = tensor("q_complex_9")]; + tensor var_2015 = const()[name = tensor("op_2015"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_9 = reshape(shape = var_2015, x = k_17)[name = tensor("k_complex_9")]; + tensor var_2019_begin_0 = const()[name = tensor("op_2019_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2019_end_0 = const()[name = tensor("op_2019_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2019_end_mask_0 = const()[name = tensor("op_2019_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2019_squeeze_mask_0 = const()[name = tensor("op_2019_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2019 = slice_by_index(begin = var_2019_begin_0, end = var_2019_end_0, end_mask = var_2019_end_mask_0, squeeze_mask = var_2019_squeeze_mask_0, x = q_complex_9)[name = tensor("op_2019")]; + tensor var_2027_begin_0 = const()[name = tensor("op_2027_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2027_end_0 = const()[name = tensor("op_2027_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2027_end_mask_0 = const()[name = tensor("op_2027_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2027_squeeze_mask_0 = const()[name = tensor("op_2027_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2027 = slice_by_index(begin = var_2027_begin_0, end = var_2027_end_0, end_mask = var_2027_end_mask_0, squeeze_mask = var_2027_squeeze_mask_0, x = q_complex_9)[name = tensor("op_2027")]; + tensor var_2035_begin_0 = const()[name = tensor("op_2035_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2035_end_0 = const()[name = tensor("op_2035_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2035_end_mask_0 = const()[name = tensor("op_2035_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2035_squeeze_mask_0 = const()[name = tensor("op_2035_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2035 = slice_by_index(begin = var_2035_begin_0, end = var_2035_end_0, end_mask = var_2035_end_mask_0, squeeze_mask = var_2035_squeeze_mask_0, x = k_complex_9)[name = tensor("op_2035")]; + tensor var_2043_begin_0 = const()[name = tensor("op_2043_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2043_end_0 = const()[name = tensor("op_2043_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2043_end_mask_0 = const()[name = tensor("op_2043_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2043_squeeze_mask_0 = const()[name = tensor("op_2043_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2043 = slice_by_index(begin = var_2043_begin_0, end = var_2043_end_0, end_mask = var_2043_end_mask_0, squeeze_mask = var_2043_squeeze_mask_0, x = k_complex_9)[name = tensor("op_2043")]; + tensor var_2049 = mul(x = freqs_9, y = ts_29)[name = tensor("op_2049")]; + tensor rotr_9 = cos(x = var_2049)[name = tensor("rotr_9")]; + tensor roti_9 = sin(x = var_2049)[name = tensor("roti_9")]; + tensor var_2053 = mul(x = var_2019, y = rotr_9)[name = tensor("op_2053")]; + tensor var_2054 = mul(x = var_2027, y = roti_9)[name = tensor("op_2054")]; + tensor qor_17 = sub(x = var_2053, y = var_2054)[name = tensor("qor_17")]; + tensor var_2057 = mul(x = var_2019, y = roti_9)[name = tensor("op_2057")]; + tensor var_2058 = mul(x = var_2027, y = rotr_9)[name = tensor("op_2058")]; + tensor qoi_17 = add(x = var_2057, y = var_2058)[name = tensor("qoi_17")]; + tensor var_2061 = mul(x = var_2035, y = rotr_9)[name = tensor("op_2061")]; + tensor var_2062 = mul(x = var_2043, y = roti_9)[name = tensor("op_2062")]; + tensor kor_17 = sub(x = var_2061, y = var_2062)[name = tensor("kor_17")]; + tensor var_2065 = mul(x = var_2035, y = roti_9)[name = tensor("op_2065")]; + tensor var_2066 = mul(x = var_2043, y = rotr_9)[name = tensor("op_2066")]; + tensor koi_17 = add(x = var_2065, y = var_2066)[name = tensor("koi_17")]; + tensor qo_9_axis_0 = const()[name = tensor("qo_9_axis_0"), val = tensor(-1)]; + tensor qo_9 = stack(axis = qo_9_axis_0, values = (qor_17, qoi_17))[name = tensor("qo_9")]; + tensor ko_9_axis_0 = const()[name = tensor("ko_9_axis_0"), val = tensor(-1)]; + tensor ko_9 = stack(axis = ko_9_axis_0, values = (kor_17, koi_17))[name = tensor("ko_9")]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1, 16, 64])]; + tensor q_27 = reshape(shape = var_2095, x = qo_9)[name = tensor("q_27")]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1, 16, 64])]; + tensor k_19 = reshape(shape = var_2097, x = ko_9)[name = tensor("k_19")]; + tensor _inversed_2119_y_0 = const()[name = tensor("_inversed_2119_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2119 = mul(x = ts_29, y = _inversed_2119_y_0)[name = tensor("_inversed_2119")]; + tensor var_2120 = floor(x = _inversed_2119)[name = tensor("op_2120")]; + tensor var_2121 = const()[name = tensor("op_2121"), val = tensor(0x1p+9)]; + tensor var_2122 = mul(x = var_2120, y = var_2121)[name = tensor("op_2122")]; + tensor write_indices_float_19 = sub(x = ts_29, y = var_2122)[name = tensor("write_indices_float_19")]; + tensor var_2129_dtype_0 = const()[name = tensor("op_2129_dtype_0"), val = tensor("int32")]; + tensor write_indices_9_reps_0 = const()[name = tensor("write_indices_9_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2129 = cast(dtype = var_2129_dtype_0, x = write_indices_float_19)[name = tensor("cast_442")]; + tensor write_indices_9 = tile(reps = write_indices_9_reps_0, x = var_2129)[name = tensor("write_indices_9")]; + tensor var_2137_begin_0 = const()[name = tensor("op_2137_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2137_end_0 = const()[name = tensor("op_2137_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2137_end_mask_0 = const()[name = tensor("op_2137_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2137_squeeze_mask_0 = const()[name = tensor("op_2137_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2137 = slice_by_index(begin = var_2137_begin_0, end = var_2137_end_0, end_mask = var_2137_end_mask_0, squeeze_mask = var_2137_squeeze_mask_0, x = cache4)[name = tensor("op_2137")]; + tensor var_2139_axis_0 = const()[name = tensor("op_2139_axis_0"), val = tensor(1)]; + tensor var_2139_mode_0 = const()[name = tensor("op_2139_mode_0"), val = tensor("update")]; + tensor var_2139_validate_indices_0 = const()[name = tensor("op_2139_validate_indices_0"), val = tensor(false)]; + tensor var_2139 = scatter_along_axis(axis = var_2139_axis_0, data = var_2137, indices = write_indices_9, mode = var_2139_mode_0, updates = k_19, validate_indices = var_2139_validate_indices_0)[name = tensor("op_2139")]; + tensor concat_29 = const()[name = tensor("concat_29"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_30 = const()[name = tensor("concat_30"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_54 = const()[name = tensor("shape_54"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_8 = const()[name = tensor("reduce_prod_8"), val = tensor(1048576)]; + tensor range_1d_8_start_0 = const()[name = tensor("range_1d_8_start_0"), val = tensor(0)]; + tensor range_1d_8_step_0 = const()[name = tensor("range_1d_8_step_0"), val = tensor(1)]; + tensor range_1d_8 = range_1d(end = reduce_prod_8, start = range_1d_8_start_0, step = range_1d_8_step_0)[name = tensor("range_1d_8")]; + tensor reshape_40 = reshape(shape = shape_54, x = range_1d_8)[name = tensor("reshape_40")]; + tensor slice_by_index_8 = slice_by_index(begin = concat_29, begin_mask = new_cache_9_internal_tensor_assign_1_begin_mask_0, end = concat_30, end_mask = new_cache_9_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_1_stride_0, x = reshape_40)[name = tensor("slice_by_index_8")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([-1])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = slice_by_index_8)[name = tensor("reshape_41")]; + tensor reshape_42_shape_0 = const()[name = tensor("reshape_42_shape_0"), val = tensor([-1])]; + tensor reshape_42 = reshape(shape = reshape_42_shape_0, x = var_2139)[name = tensor("reshape_42")]; + tensor reshape_43_shape_0 = const()[name = tensor("reshape_43_shape_0"), val = tensor([-1])]; + tensor reshape_43 = reshape(shape = reshape_43_shape_0, x = cache4)[name = tensor("reshape_43")]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8_validate_indices_0 = const()[name = tensor("scatter_8_validate_indices_0"), val = tensor(false)]; + tensor scatter_8 = scatter(axis = scatter_8_axis_0, data = reshape_43, indices = reshape_41, mode = scatter_8_mode_0, updates = reshape_42, validate_indices = scatter_8_validate_indices_0)[name = tensor("scatter_8")]; + tensor reshape_44 = reshape(shape = shape_54, x = scatter_8)[name = tensor("reshape_44")]; + tensor var_2147_begin_0 = const()[name = tensor("op_2147_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2147_end_0 = const()[name = tensor("op_2147_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2147_end_mask_0 = const()[name = tensor("op_2147_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2147_squeeze_mask_0 = const()[name = tensor("op_2147_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2147 = slice_by_index(begin = var_2147_begin_0, end = var_2147_end_0, end_mask = var_2147_end_mask_0, squeeze_mask = var_2147_squeeze_mask_0, x = reshape_44)[name = tensor("op_2147")]; + tensor var_2149_axis_0 = const()[name = tensor("op_2149_axis_0"), val = tensor(1)]; + tensor var_2149_mode_0 = const()[name = tensor("op_2149_mode_0"), val = tensor("update")]; + tensor var_2149_validate_indices_0 = const()[name = tensor("op_2149_validate_indices_0"), val = tensor(false)]; + tensor var_2149 = scatter_along_axis(axis = var_2149_axis_0, data = var_2147, indices = write_indices_9, mode = var_2149_mode_0, updates = v_9, validate_indices = var_2149_validate_indices_0)[name = tensor("op_2149")]; + tensor concat_31 = const()[name = tensor("concat_31"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_32 = const()[name = tensor("concat_32"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_55 = const()[name = tensor("shape_55"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_9 = const()[name = tensor("reduce_prod_9"), val = tensor(1048576)]; + tensor range_1d_9_start_0 = const()[name = tensor("range_1d_9_start_0"), val = tensor(0)]; + tensor range_1d_9_step_0 = const()[name = tensor("range_1d_9_step_0"), val = tensor(1)]; + tensor range_1d_9 = range_1d(end = reduce_prod_9, start = range_1d_9_start_0, step = range_1d_9_step_0)[name = tensor("range_1d_9")]; + tensor reshape_45 = reshape(shape = shape_55, x = range_1d_9)[name = tensor("reshape_45")]; + tensor slice_by_index_9 = slice_by_index(begin = concat_31, begin_mask = new_cache_9_internal_tensor_assign_2_begin_mask_0, end = concat_32, end_mask = new_cache_9_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_2_stride_0, x = reshape_45)[name = tensor("slice_by_index_9")]; + tensor reshape_46_shape_0 = const()[name = tensor("reshape_46_shape_0"), val = tensor([-1])]; + tensor reshape_46 = reshape(shape = reshape_46_shape_0, x = slice_by_index_9)[name = tensor("reshape_46")]; + tensor reshape_47_shape_0 = const()[name = tensor("reshape_47_shape_0"), val = tensor([-1])]; + tensor reshape_47 = reshape(shape = reshape_47_shape_0, x = var_2149)[name = tensor("reshape_47")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([-1])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = reshape_44)[name = tensor("reshape_48")]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9_validate_indices_0 = const()[name = tensor("scatter_9_validate_indices_0"), val = tensor(false)]; + tensor scatter_9 = scatter(axis = scatter_9_axis_0, data = reshape_48, indices = reshape_46, mode = scatter_9_mode_0, updates = reshape_47, validate_indices = scatter_9_validate_indices_0)[name = tensor("scatter_9")]; + tensor new_cache_9_internal_tensor_assign_2 = reshape(shape = shape_55, x = scatter_9)[name = tensor("reshape_49")]; + tensor keys_25_begin_0 = const()[name = tensor("keys_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_25_end_0 = const()[name = tensor("keys_25_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_25_end_mask_0 = const()[name = tensor("keys_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_25_squeeze_mask_0 = const()[name = tensor("keys_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_25 = slice_by_index(begin = keys_25_begin_0, end = keys_25_end_0, end_mask = keys_25_end_mask_0, squeeze_mask = keys_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("keys_25")]; + tensor values_25_begin_0 = const()[name = tensor("values_25_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_25_end_0 = const()[name = tensor("values_25_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_25_end_mask_0 = const()[name = tensor("values_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_25_squeeze_mask_0 = const()[name = tensor("values_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_25 = slice_by_index(begin = values_25_begin_0, end = values_25_end_0, end_mask = values_25_end_mask_0, squeeze_mask = values_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("values_25")]; + tensor var_2161 = not_equal(x = keys_25, y = keys_25)[name = tensor("op_2161")]; + tensor keys_27 = select(a = var_491, b = keys_25, cond = var_2161)[name = tensor("keys_27")]; + tensor var_2169 = not_equal(x = values_25, y = values_25)[name = tensor("op_2169")]; + tensor values_27 = select(a = var_491, b = values_25, cond = var_2169)[name = tensor("values_27")]; + tensor var_2193 = const()[name = tensor("op_2193"), val = tensor([0, 2, 1, 3])]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1, 1])]; + tensor var_2207 = reshape(shape = var_2206, x = position4)[name = tensor("op_2207")]; + tensor var_2224 = const()[name = tensor("op_2224"), val = tensor(0x1p+0)]; + tensor valid_len_9 = add(x = var_2207, y = var_2224)[name = tensor("valid_len_9")]; + tensor valid_mask_9 = less(x = k_positions_1_promoted, y = valid_len_9)[name = tensor("valid_mask_9")]; + tensor causal_mask_9 = less_equal(x = k_positions_1_promoted, y = var_2207)[name = tensor("causal_mask_9")]; + tensor attn_mask_17 = logical_and(x = valid_mask_9, y = causal_mask_9)[name = tensor("attn_mask_17")]; + tensor attn_mask_19_axes_0 = const()[name = tensor("attn_mask_19_axes_0"), val = tensor([1])]; + tensor attn_mask_19 = expand_dims(axes = attn_mask_19_axes_0, x = attn_mask_17)[name = tensor("attn_mask_19")]; + tensor var_2236 = const()[name = tensor("op_2236"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2242_transpose_x_0 = const()[name = tensor("op_2242_transpose_x_0"), val = tensor(false)]; + tensor var_2242_transpose_y_0 = const()[name = tensor("op_2242_transpose_y_0"), val = tensor(false)]; + tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = keys_27)[name = tensor("transpose_188")]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = q_27)[name = tensor("transpose_189")]; + tensor var_2242 = matmul(transpose_x = var_2242_transpose_x_0, transpose_y = var_2242_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor("op_2242")]; + tensor attn_weights_25 = mul(x = var_2242, y = var_2236)[name = tensor("attn_weights_25")]; + tensor var_2244 = logical_not(x = attn_mask_19)[name = tensor("op_2244")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_27 = select(a = var_2245, b = attn_weights_25, cond = var_2244)[name = tensor("attn_weights_27")]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor(-1)]; + tensor attn_weights_29 = softmax(axis = var_2247, x = attn_weights_27)[name = tensor("attn_weights_29")]; + tensor attn_output_9_transpose_x_0 = const()[name = tensor("attn_output_9_transpose_x_0"), val = tensor(false)]; + tensor attn_output_9_transpose_y_0 = const()[name = tensor("attn_output_9_transpose_y_0"), val = tensor(false)]; + tensor values_29 = transpose(perm = var_2193, x = values_27)[name = tensor("transpose_190")]; + tensor attn_output_9 = matmul(transpose_x = attn_output_9_transpose_x_0, transpose_y = attn_output_9_transpose_y_0, x = attn_weights_29, y = values_29)[name = tensor("attn_output_9")]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([0, 2, 1, 3])]; + tensor var_2258 = const()[name = tensor("op_2258"), val = tensor([1, 1, 1024])]; + tensor var_2256 = transpose(perm = var_2255, x = attn_output_9)[name = tensor("transpose_187")]; + tensor input_43 = reshape(shape = var_2258, x = var_2256)[name = tensor("input_43")]; + tensor attn_out_9 = linear(bias = linear_1_bias_0, weight = attn4_out_proj_weight, x = input_43)[name = tensor("linear_17")]; + tensor var_2264 = const()[name = tensor("op_2264"), val = tensor(0x1p+0)]; + tensor var_2265 = add(x = position4, y = var_2264)[name = tensor("op_2265")]; + tensor input_45 = add(x = input_41, y = attn_out_9)[name = tensor("input_45")]; + tensor var_2269 = const()[name = tensor("op_2269"), val = tensor(0x1.4f8b58p-17)]; + tensor input_47_axes_0 = const()[name = tensor("input_47_axes_0"), val = tensor([-1])]; + tensor input_47 = layer_norm(axes = input_47_axes_0, beta = norm4_2_bias, epsilon = var_2269, gamma = norm4_2_weight, x = input_45)[name = tensor("input_47")]; + tensor var_2277 = linear(bias = linear_2_bias_0, weight = linear4_1_weight, x = input_47)[name = tensor("linear_18")]; + tensor input_49_mode_0 = const()[name = tensor("input_49_mode_0"), val = tensor("EXACT")]; + tensor input_49 = gelu(mode = input_49_mode_0, x = var_2277)[name = tensor("input_49")]; + tensor ffn_out_9 = linear(bias = linear_1_bias_0, weight = linear4_2_weight, x = input_49)[name = tensor("linear_19")]; + tensor input_51 = add(x = input_45, y = ffn_out_9)[name = tensor("input_51")]; + tensor var_2286 = const()[name = tensor("op_2286"), val = tensor(0x1.4f8b58p-17)]; + tensor x_11_axes_0 = const()[name = tensor("x_11_axes_0"), val = tensor([-1])]; + tensor x_11 = layer_norm(axes = x_11_axes_0, beta = norm5_1_bias, epsilon = var_2286, gamma = norm5_1_weight, x = input_51)[name = tensor("x_11")]; + tensor var_2318 = linear(bias = linear_0_bias_0, weight = attn5_in_proj_weight, x = x_11)[name = tensor("linear_20")]; + tensor var_2322 = const()[name = tensor("op_2322"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_11 = reshape(shape = var_2322, x = var_2318)[name = tensor("qkv_11")]; + tensor q_31_begin_0 = const()[name = tensor("q_31_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_31_end_0 = const()[name = tensor("q_31_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_31_end_mask_0 = const()[name = tensor("q_31_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_31_squeeze_mask_0 = const()[name = tensor("q_31_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_31 = slice_by_index(begin = q_31_begin_0, end = q_31_end_0, end_mask = q_31_end_mask_0, squeeze_mask = q_31_squeeze_mask_0, x = qkv_11)[name = tensor("q_31")]; + tensor k_21_begin_0 = const()[name = tensor("k_21_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_21_end_0 = const()[name = tensor("k_21_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_21_end_mask_0 = const()[name = tensor("k_21_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_21_squeeze_mask_0 = const()[name = tensor("k_21_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_21 = slice_by_index(begin = k_21_begin_0, end = k_21_end_0, end_mask = k_21_end_mask_0, squeeze_mask = k_21_squeeze_mask_0, x = qkv_11)[name = tensor("k_21")]; + tensor v_11_begin_0 = const()[name = tensor("v_11_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_11_end_0 = const()[name = tensor("v_11_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_11_end_mask_0 = const()[name = tensor("v_11_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_11_squeeze_mask_0 = const()[name = tensor("v_11_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_11 = slice_by_index(begin = v_11_begin_0, end = v_11_end_0, end_mask = v_11_end_mask_0, squeeze_mask = v_11_squeeze_mask_0, x = qkv_11)[name = tensor("v_11")]; + tensor freqs_11 = const()[name = tensor("freqs_11"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172741120)))]; + tensor var_2426 = const()[name = tensor("op_2426"), val = tensor([1, 1, 1, 1])]; + tensor ts_35 = reshape(shape = var_2426, x = position5)[name = tensor("ts_35")]; + tensor var_2430 = const()[name = tensor("op_2430"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_11 = reshape(shape = var_2430, x = q_31)[name = tensor("q_complex_11")]; + tensor var_2434 = const()[name = tensor("op_2434"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_11 = reshape(shape = var_2434, x = k_21)[name = tensor("k_complex_11")]; + tensor var_2438_begin_0 = const()[name = tensor("op_2438_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2438_end_0 = const()[name = tensor("op_2438_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2438_end_mask_0 = const()[name = tensor("op_2438_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2438_squeeze_mask_0 = const()[name = tensor("op_2438_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2438 = slice_by_index(begin = var_2438_begin_0, end = var_2438_end_0, end_mask = var_2438_end_mask_0, squeeze_mask = var_2438_squeeze_mask_0, x = q_complex_11)[name = tensor("op_2438")]; + tensor var_2446_begin_0 = const()[name = tensor("op_2446_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2446_end_0 = const()[name = tensor("op_2446_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2446_end_mask_0 = const()[name = tensor("op_2446_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2446_squeeze_mask_0 = const()[name = tensor("op_2446_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2446 = slice_by_index(begin = var_2446_begin_0, end = var_2446_end_0, end_mask = var_2446_end_mask_0, squeeze_mask = var_2446_squeeze_mask_0, x = q_complex_11)[name = tensor("op_2446")]; + tensor var_2454_begin_0 = const()[name = tensor("op_2454_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2454_end_0 = const()[name = tensor("op_2454_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2454_end_mask_0 = const()[name = tensor("op_2454_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2454_squeeze_mask_0 = const()[name = tensor("op_2454_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2454 = slice_by_index(begin = var_2454_begin_0, end = var_2454_end_0, end_mask = var_2454_end_mask_0, squeeze_mask = var_2454_squeeze_mask_0, x = k_complex_11)[name = tensor("op_2454")]; + tensor var_2462_begin_0 = const()[name = tensor("op_2462_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2462_end_0 = const()[name = tensor("op_2462_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2462_end_mask_0 = const()[name = tensor("op_2462_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2462_squeeze_mask_0 = const()[name = tensor("op_2462_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2462 = slice_by_index(begin = var_2462_begin_0, end = var_2462_end_0, end_mask = var_2462_end_mask_0, squeeze_mask = var_2462_squeeze_mask_0, x = k_complex_11)[name = tensor("op_2462")]; + tensor var_2468 = mul(x = freqs_11, y = ts_35)[name = tensor("op_2468")]; + tensor rotr_11 = cos(x = var_2468)[name = tensor("rotr_11")]; + tensor roti_11 = sin(x = var_2468)[name = tensor("roti_11")]; + tensor var_2472 = mul(x = var_2438, y = rotr_11)[name = tensor("op_2472")]; + tensor var_2473 = mul(x = var_2446, y = roti_11)[name = tensor("op_2473")]; + tensor qor_21 = sub(x = var_2472, y = var_2473)[name = tensor("qor_21")]; + tensor var_2476 = mul(x = var_2438, y = roti_11)[name = tensor("op_2476")]; + tensor var_2477 = mul(x = var_2446, y = rotr_11)[name = tensor("op_2477")]; + tensor qoi_21 = add(x = var_2476, y = var_2477)[name = tensor("qoi_21")]; + tensor var_2480 = mul(x = var_2454, y = rotr_11)[name = tensor("op_2480")]; + tensor var_2481 = mul(x = var_2462, y = roti_11)[name = tensor("op_2481")]; + tensor kor_21 = sub(x = var_2480, y = var_2481)[name = tensor("kor_21")]; + tensor var_2484 = mul(x = var_2454, y = roti_11)[name = tensor("op_2484")]; + tensor var_2485 = mul(x = var_2462, y = rotr_11)[name = tensor("op_2485")]; + tensor koi_21 = add(x = var_2484, y = var_2485)[name = tensor("koi_21")]; + tensor qo_11_axis_0 = const()[name = tensor("qo_11_axis_0"), val = tensor(-1)]; + tensor qo_11 = stack(axis = qo_11_axis_0, values = (qor_21, qoi_21))[name = tensor("qo_11")]; + tensor ko_11_axis_0 = const()[name = tensor("ko_11_axis_0"), val = tensor(-1)]; + tensor ko_11 = stack(axis = ko_11_axis_0, values = (kor_21, koi_21))[name = tensor("ko_11")]; + tensor var_2514 = const()[name = tensor("op_2514"), val = tensor([1, 1, 16, 64])]; + tensor q_33 = reshape(shape = var_2514, x = qo_11)[name = tensor("q_33")]; + tensor var_2516 = const()[name = tensor("op_2516"), val = tensor([1, 1, 16, 64])]; + tensor k_23 = reshape(shape = var_2516, x = ko_11)[name = tensor("k_23")]; + tensor _inversed_2538_y_0 = const()[name = tensor("_inversed_2538_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2538 = mul(x = ts_35, y = _inversed_2538_y_0)[name = tensor("_inversed_2538")]; + tensor var_2539 = floor(x = _inversed_2538)[name = tensor("op_2539")]; + tensor var_2540 = const()[name = tensor("op_2540"), val = tensor(0x1p+9)]; + tensor var_2541 = mul(x = var_2539, y = var_2540)[name = tensor("op_2541")]; + tensor write_indices_float_23 = sub(x = ts_35, y = var_2541)[name = tensor("write_indices_float_23")]; + tensor var_2548_dtype_0 = const()[name = tensor("op_2548_dtype_0"), val = tensor("int32")]; + tensor write_indices_11_reps_0 = const()[name = tensor("write_indices_11_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2548 = cast(dtype = var_2548_dtype_0, x = write_indices_float_23)[name = tensor("cast_441")]; + tensor write_indices_11 = tile(reps = write_indices_11_reps_0, x = var_2548)[name = tensor("write_indices_11")]; + tensor var_2556_begin_0 = const()[name = tensor("op_2556_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2556_end_0 = const()[name = tensor("op_2556_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2556_end_mask_0 = const()[name = tensor("op_2556_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2556_squeeze_mask_0 = const()[name = tensor("op_2556_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2556 = slice_by_index(begin = var_2556_begin_0, end = var_2556_end_0, end_mask = var_2556_end_mask_0, squeeze_mask = var_2556_squeeze_mask_0, x = cache5)[name = tensor("op_2556")]; + tensor var_2558_axis_0 = const()[name = tensor("op_2558_axis_0"), val = tensor(1)]; + tensor var_2558_mode_0 = const()[name = tensor("op_2558_mode_0"), val = tensor("update")]; + tensor var_2558_validate_indices_0 = const()[name = tensor("op_2558_validate_indices_0"), val = tensor(false)]; + tensor var_2558 = scatter_along_axis(axis = var_2558_axis_0, data = var_2556, indices = write_indices_11, mode = var_2558_mode_0, updates = k_23, validate_indices = var_2558_validate_indices_0)[name = tensor("op_2558")]; + tensor concat_36 = const()[name = tensor("concat_36"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_11_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_11_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_56 = const()[name = tensor("shape_56"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_10 = const()[name = tensor("reduce_prod_10"), val = tensor(1048576)]; + tensor range_1d_10_start_0 = const()[name = tensor("range_1d_10_start_0"), val = tensor(0)]; + tensor range_1d_10_step_0 = const()[name = tensor("range_1d_10_step_0"), val = tensor(1)]; + tensor range_1d_10 = range_1d(end = reduce_prod_10, start = range_1d_10_start_0, step = range_1d_10_step_0)[name = tensor("range_1d_10")]; + tensor reshape_50 = reshape(shape = shape_56, x = range_1d_10)[name = tensor("reshape_50")]; + tensor slice_by_index_10 = slice_by_index(begin = concat_36, begin_mask = new_cache_11_internal_tensor_assign_1_begin_mask_0, end = concat_37, end_mask = new_cache_11_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_11_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_11_internal_tensor_assign_1_stride_0, x = reshape_50)[name = tensor("slice_by_index_10")]; + tensor reshape_51_shape_0 = const()[name = tensor("reshape_51_shape_0"), val = tensor([-1])]; + tensor reshape_51 = reshape(shape = reshape_51_shape_0, x = slice_by_index_10)[name = tensor("reshape_51")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([-1])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_2558)[name = tensor("reshape_52")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([-1])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = cache5)[name = tensor("reshape_53")]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10_validate_indices_0 = const()[name = tensor("scatter_10_validate_indices_0"), val = tensor(false)]; + tensor scatter_10 = scatter(axis = scatter_10_axis_0, data = reshape_53, indices = reshape_51, mode = scatter_10_mode_0, updates = reshape_52, validate_indices = scatter_10_validate_indices_0)[name = tensor("scatter_10")]; + tensor reshape_54 = reshape(shape = shape_56, x = scatter_10)[name = tensor("reshape_54")]; + tensor var_2566_begin_0 = const()[name = tensor("op_2566_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2566_end_0 = const()[name = tensor("op_2566_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2566_end_mask_0 = const()[name = tensor("op_2566_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2566_squeeze_mask_0 = const()[name = tensor("op_2566_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2566 = slice_by_index(begin = var_2566_begin_0, end = var_2566_end_0, end_mask = var_2566_end_mask_0, squeeze_mask = var_2566_squeeze_mask_0, x = reshape_54)[name = tensor("op_2566")]; + tensor var_2568_axis_0 = const()[name = tensor("op_2568_axis_0"), val = tensor(1)]; + tensor var_2568_mode_0 = const()[name = tensor("op_2568_mode_0"), val = tensor("update")]; + tensor var_2568_validate_indices_0 = const()[name = tensor("op_2568_validate_indices_0"), val = tensor(false)]; + tensor var_2568 = scatter_along_axis(axis = var_2568_axis_0, data = var_2566, indices = write_indices_11, mode = var_2568_mode_0, updates = v_11, validate_indices = var_2568_validate_indices_0)[name = tensor("op_2568")]; + tensor concat_38 = const()[name = tensor("concat_38"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_39 = const()[name = tensor("concat_39"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_11_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_11_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_57 = const()[name = tensor("shape_57"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_11 = const()[name = tensor("reduce_prod_11"), val = tensor(1048576)]; + tensor range_1d_11_start_0 = const()[name = tensor("range_1d_11_start_0"), val = tensor(0)]; + tensor range_1d_11_step_0 = const()[name = tensor("range_1d_11_step_0"), val = tensor(1)]; + tensor range_1d_11 = range_1d(end = reduce_prod_11, start = range_1d_11_start_0, step = range_1d_11_step_0)[name = tensor("range_1d_11")]; + tensor reshape_55 = reshape(shape = shape_57, x = range_1d_11)[name = tensor("reshape_55")]; + tensor slice_by_index_11 = slice_by_index(begin = concat_38, begin_mask = new_cache_11_internal_tensor_assign_2_begin_mask_0, end = concat_39, end_mask = new_cache_11_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_11_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_11_internal_tensor_assign_2_stride_0, x = reshape_55)[name = tensor("slice_by_index_11")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([-1])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = slice_by_index_11)[name = tensor("reshape_56")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([-1])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = var_2568)[name = tensor("reshape_57")]; + tensor reshape_58_shape_0 = const()[name = tensor("reshape_58_shape_0"), val = tensor([-1])]; + tensor reshape_58 = reshape(shape = reshape_58_shape_0, x = reshape_54)[name = tensor("reshape_58")]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11_validate_indices_0 = const()[name = tensor("scatter_11_validate_indices_0"), val = tensor(false)]; + tensor scatter_11 = scatter(axis = scatter_11_axis_0, data = reshape_58, indices = reshape_56, mode = scatter_11_mode_0, updates = reshape_57, validate_indices = scatter_11_validate_indices_0)[name = tensor("scatter_11")]; + tensor new_cache_11_internal_tensor_assign_2 = reshape(shape = shape_57, x = scatter_11)[name = tensor("reshape_59")]; + tensor keys_31_begin_0 = const()[name = tensor("keys_31_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_31_end_0 = const()[name = tensor("keys_31_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_31_end_mask_0 = const()[name = tensor("keys_31_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_31_squeeze_mask_0 = const()[name = tensor("keys_31_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_31 = slice_by_index(begin = keys_31_begin_0, end = keys_31_end_0, end_mask = keys_31_end_mask_0, squeeze_mask = keys_31_squeeze_mask_0, x = new_cache_11_internal_tensor_assign_2)[name = tensor("keys_31")]; + tensor values_31_begin_0 = const()[name = tensor("values_31_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_31_end_0 = const()[name = tensor("values_31_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_31_end_mask_0 = const()[name = tensor("values_31_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_31_squeeze_mask_0 = const()[name = tensor("values_31_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_31 = slice_by_index(begin = values_31_begin_0, end = values_31_end_0, end_mask = values_31_end_mask_0, squeeze_mask = values_31_squeeze_mask_0, x = new_cache_11_internal_tensor_assign_2)[name = tensor("values_31")]; + tensor var_2580 = not_equal(x = keys_31, y = keys_31)[name = tensor("op_2580")]; + tensor keys_33 = select(a = var_491, b = keys_31, cond = var_2580)[name = tensor("keys_33")]; + tensor var_2588 = not_equal(x = values_31, y = values_31)[name = tensor("op_2588")]; + tensor values_33 = select(a = var_491, b = values_31, cond = var_2588)[name = tensor("values_33")]; + tensor var_2612 = const()[name = tensor("op_2612"), val = tensor([0, 2, 1, 3])]; + tensor var_2625 = const()[name = tensor("op_2625"), val = tensor([1, 1, 1])]; + tensor var_2626 = reshape(shape = var_2625, x = position5)[name = tensor("op_2626")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor(0x1p+0)]; + tensor valid_len_11 = add(x = var_2626, y = var_2643)[name = tensor("valid_len_11")]; + tensor valid_mask_11 = less(x = k_positions_1_promoted, y = valid_len_11)[name = tensor("valid_mask_11")]; + tensor causal_mask_11 = less_equal(x = k_positions_1_promoted, y = var_2626)[name = tensor("causal_mask_11")]; + tensor attn_mask_21 = logical_and(x = valid_mask_11, y = causal_mask_11)[name = tensor("attn_mask_21")]; + tensor attn_mask_23_axes_0 = const()[name = tensor("attn_mask_23_axes_0"), val = tensor([1])]; + tensor attn_mask_23 = expand_dims(axes = attn_mask_23_axes_0, x = attn_mask_21)[name = tensor("attn_mask_23")]; + tensor var_2655 = const()[name = tensor("op_2655"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2661_transpose_x_0 = const()[name = tensor("op_2661_transpose_x_0"), val = tensor(false)]; + tensor var_2661_transpose_y_0 = const()[name = tensor("op_2661_transpose_y_0"), val = tensor(false)]; + tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = keys_33)[name = tensor("transpose_184")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = q_33)[name = tensor("transpose_185")]; + tensor var_2661 = matmul(transpose_x = var_2661_transpose_x_0, transpose_y = var_2661_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor("op_2661")]; + tensor attn_weights_31 = mul(x = var_2661, y = var_2655)[name = tensor("attn_weights_31")]; + tensor var_2663 = logical_not(x = attn_mask_23)[name = tensor("op_2663")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_33 = select(a = var_2664, b = attn_weights_31, cond = var_2663)[name = tensor("attn_weights_33")]; + tensor var_2666 = const()[name = tensor("op_2666"), val = tensor(-1)]; + tensor attn_weights_35 = softmax(axis = var_2666, x = attn_weights_33)[name = tensor("attn_weights_35")]; + tensor attn_output_11_transpose_x_0 = const()[name = tensor("attn_output_11_transpose_x_0"), val = tensor(false)]; + tensor attn_output_11_transpose_y_0 = const()[name = tensor("attn_output_11_transpose_y_0"), val = tensor(false)]; + tensor values_35 = transpose(perm = var_2612, x = values_33)[name = tensor("transpose_186")]; + tensor attn_output_11 = matmul(transpose_x = attn_output_11_transpose_x_0, transpose_y = attn_output_11_transpose_y_0, x = attn_weights_35, y = values_35)[name = tensor("attn_output_11")]; + tensor var_2674 = const()[name = tensor("op_2674"), val = tensor([0, 2, 1, 3])]; + tensor var_2677 = const()[name = tensor("op_2677"), val = tensor([1, 1, 1024])]; + tensor var_2675 = transpose(perm = var_2674, x = attn_output_11)[name = tensor("transpose_183")]; + tensor input_53 = reshape(shape = var_2677, x = var_2675)[name = tensor("input_53")]; + tensor attn_out_11 = linear(bias = linear_1_bias_0, weight = attn5_out_proj_weight, x = input_53)[name = tensor("linear_21")]; + tensor var_2683 = const()[name = tensor("op_2683"), val = tensor(0x1p+0)]; + tensor var_2684 = add(x = position5, y = var_2683)[name = tensor("op_2684")]; + tensor input_55 = add(x = input_51, y = attn_out_11)[name = tensor("input_55")]; + tensor var_2688 = const()[name = tensor("op_2688"), val = tensor(0x1.4f8b58p-17)]; + tensor input_57_axes_0 = const()[name = tensor("input_57_axes_0"), val = tensor([-1])]; + tensor input_57 = layer_norm(axes = input_57_axes_0, beta = norm5_2_bias, epsilon = var_2688, gamma = norm5_2_weight, x = input_55)[name = tensor("input_57")]; + tensor var_2696 = linear(bias = linear_2_bias_0, weight = linear5_1_weight, x = input_57)[name = tensor("linear_22")]; + tensor input_59_mode_0 = const()[name = tensor("input_59_mode_0"), val = tensor("EXACT")]; + tensor input_59 = gelu(mode = input_59_mode_0, x = var_2696)[name = tensor("input_59")]; + tensor ffn_out_11 = linear(bias = linear_1_bias_0, weight = linear5_2_weight, x = input_59)[name = tensor("linear_23")]; + tensor input_61 = add(x = input_55, y = ffn_out_11)[name = tensor("input_61")]; + tensor var_2705 = const()[name = tensor("op_2705"), val = tensor(0x1.4f8b58p-17)]; + tensor x_13_axes_0 = const()[name = tensor("x_13_axes_0"), val = tensor([-1])]; + tensor x_13 = layer_norm(axes = x_13_axes_0, beta = norm6_1_bias, epsilon = var_2705, gamma = norm6_1_weight, x = input_61)[name = tensor("x_13")]; + tensor var_2737 = linear(bias = linear_0_bias_0, weight = attn6_in_proj_weight, x = x_13)[name = tensor("linear_24")]; + tensor var_2741 = const()[name = tensor("op_2741"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_13 = reshape(shape = var_2741, x = var_2737)[name = tensor("qkv_13")]; + tensor q_37_begin_0 = const()[name = tensor("q_37_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_37_end_0 = const()[name = tensor("q_37_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_37_end_mask_0 = const()[name = tensor("q_37_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_37_squeeze_mask_0 = const()[name = tensor("q_37_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_37 = slice_by_index(begin = q_37_begin_0, end = q_37_end_0, end_mask = q_37_end_mask_0, squeeze_mask = q_37_squeeze_mask_0, x = qkv_13)[name = tensor("q_37")]; + tensor k_25_begin_0 = const()[name = tensor("k_25_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_25_end_0 = const()[name = tensor("k_25_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_25_end_mask_0 = const()[name = tensor("k_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_25_squeeze_mask_0 = const()[name = tensor("k_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_25 = slice_by_index(begin = k_25_begin_0, end = k_25_end_0, end_mask = k_25_end_mask_0, squeeze_mask = k_25_squeeze_mask_0, x = qkv_13)[name = tensor("k_25")]; + tensor v_13_begin_0 = const()[name = tensor("v_13_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_13_end_0 = const()[name = tensor("v_13_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_13_end_mask_0 = const()[name = tensor("v_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_13_squeeze_mask_0 = const()[name = tensor("v_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_13 = slice_by_index(begin = v_13_begin_0, end = v_13_end_0, end_mask = v_13_end_mask_0, squeeze_mask = v_13_squeeze_mask_0, x = qkv_13)[name = tensor("v_13")]; + tensor freqs_13 = const()[name = tensor("freqs_13"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172741312)))]; + tensor var_2845 = const()[name = tensor("op_2845"), val = tensor([1, 1, 1, 1])]; + tensor ts_41 = reshape(shape = var_2845, x = position6)[name = tensor("ts_41")]; + tensor var_2849 = const()[name = tensor("op_2849"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_13 = reshape(shape = var_2849, x = q_37)[name = tensor("q_complex_13")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_13 = reshape(shape = var_2853, x = k_25)[name = tensor("k_complex_13")]; + tensor var_2857_begin_0 = const()[name = tensor("op_2857_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2857_end_0 = const()[name = tensor("op_2857_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2857_end_mask_0 = const()[name = tensor("op_2857_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2857_squeeze_mask_0 = const()[name = tensor("op_2857_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2857 = slice_by_index(begin = var_2857_begin_0, end = var_2857_end_0, end_mask = var_2857_end_mask_0, squeeze_mask = var_2857_squeeze_mask_0, x = q_complex_13)[name = tensor("op_2857")]; + tensor var_2865_begin_0 = const()[name = tensor("op_2865_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2865_end_0 = const()[name = tensor("op_2865_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2865_end_mask_0 = const()[name = tensor("op_2865_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2865_squeeze_mask_0 = const()[name = tensor("op_2865_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2865 = slice_by_index(begin = var_2865_begin_0, end = var_2865_end_0, end_mask = var_2865_end_mask_0, squeeze_mask = var_2865_squeeze_mask_0, x = q_complex_13)[name = tensor("op_2865")]; + tensor var_2873_begin_0 = const()[name = tensor("op_2873_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2873_end_0 = const()[name = tensor("op_2873_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2873_end_mask_0 = const()[name = tensor("op_2873_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2873_squeeze_mask_0 = const()[name = tensor("op_2873_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2873 = slice_by_index(begin = var_2873_begin_0, end = var_2873_end_0, end_mask = var_2873_end_mask_0, squeeze_mask = var_2873_squeeze_mask_0, x = k_complex_13)[name = tensor("op_2873")]; + tensor var_2881_begin_0 = const()[name = tensor("op_2881_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2881_end_0 = const()[name = tensor("op_2881_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2881_end_mask_0 = const()[name = tensor("op_2881_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2881_squeeze_mask_0 = const()[name = tensor("op_2881_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2881 = slice_by_index(begin = var_2881_begin_0, end = var_2881_end_0, end_mask = var_2881_end_mask_0, squeeze_mask = var_2881_squeeze_mask_0, x = k_complex_13)[name = tensor("op_2881")]; + tensor var_2887 = mul(x = freqs_13, y = ts_41)[name = tensor("op_2887")]; + tensor rotr_13 = cos(x = var_2887)[name = tensor("rotr_13")]; + tensor roti_13 = sin(x = var_2887)[name = tensor("roti_13")]; + tensor var_2891 = mul(x = var_2857, y = rotr_13)[name = tensor("op_2891")]; + tensor var_2892 = mul(x = var_2865, y = roti_13)[name = tensor("op_2892")]; + tensor qor_25 = sub(x = var_2891, y = var_2892)[name = tensor("qor_25")]; + tensor var_2895 = mul(x = var_2857, y = roti_13)[name = tensor("op_2895")]; + tensor var_2896 = mul(x = var_2865, y = rotr_13)[name = tensor("op_2896")]; + tensor qoi_25 = add(x = var_2895, y = var_2896)[name = tensor("qoi_25")]; + tensor var_2899 = mul(x = var_2873, y = rotr_13)[name = tensor("op_2899")]; + tensor var_2900 = mul(x = var_2881, y = roti_13)[name = tensor("op_2900")]; + tensor kor_25 = sub(x = var_2899, y = var_2900)[name = tensor("kor_25")]; + tensor var_2903 = mul(x = var_2873, y = roti_13)[name = tensor("op_2903")]; + tensor var_2904 = mul(x = var_2881, y = rotr_13)[name = tensor("op_2904")]; + tensor koi_25 = add(x = var_2903, y = var_2904)[name = tensor("koi_25")]; + tensor qo_13_axis_0 = const()[name = tensor("qo_13_axis_0"), val = tensor(-1)]; + tensor qo_13 = stack(axis = qo_13_axis_0, values = (qor_25, qoi_25))[name = tensor("qo_13")]; + tensor ko_13_axis_0 = const()[name = tensor("ko_13_axis_0"), val = tensor(-1)]; + tensor ko_13 = stack(axis = ko_13_axis_0, values = (kor_25, koi_25))[name = tensor("ko_13")]; + tensor var_2933 = const()[name = tensor("op_2933"), val = tensor([1, 1, 16, 64])]; + tensor q_39 = reshape(shape = var_2933, x = qo_13)[name = tensor("q_39")]; + tensor var_2935 = const()[name = tensor("op_2935"), val = tensor([1, 1, 16, 64])]; + tensor k_27 = reshape(shape = var_2935, x = ko_13)[name = tensor("k_27")]; + tensor _inversed_2957_y_0 = const()[name = tensor("_inversed_2957_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2957 = mul(x = ts_41, y = _inversed_2957_y_0)[name = tensor("_inversed_2957")]; + tensor var_2958 = floor(x = _inversed_2957)[name = tensor("op_2958")]; + tensor var_2959 = const()[name = tensor("op_2959"), val = tensor(0x1p+9)]; + tensor var_2960 = mul(x = var_2958, y = var_2959)[name = tensor("op_2960")]; + tensor write_indices_float_27 = sub(x = ts_41, y = var_2960)[name = tensor("write_indices_float_27")]; + tensor var_2967_dtype_0 = const()[name = tensor("op_2967_dtype_0"), val = tensor("int32")]; + tensor write_indices_13_reps_0 = const()[name = tensor("write_indices_13_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2967 = cast(dtype = var_2967_dtype_0, x = write_indices_float_27)[name = tensor("cast_440")]; + tensor write_indices_13 = tile(reps = write_indices_13_reps_0, x = var_2967)[name = tensor("write_indices_13")]; + tensor var_2975_begin_0 = const()[name = tensor("op_2975_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2975_end_0 = const()[name = tensor("op_2975_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2975_end_mask_0 = const()[name = tensor("op_2975_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2975_squeeze_mask_0 = const()[name = tensor("op_2975_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2975 = slice_by_index(begin = var_2975_begin_0, end = var_2975_end_0, end_mask = var_2975_end_mask_0, squeeze_mask = var_2975_squeeze_mask_0, x = cache6)[name = tensor("op_2975")]; + tensor var_2977_axis_0 = const()[name = tensor("op_2977_axis_0"), val = tensor(1)]; + tensor var_2977_mode_0 = const()[name = tensor("op_2977_mode_0"), val = tensor("update")]; + tensor var_2977_validate_indices_0 = const()[name = tensor("op_2977_validate_indices_0"), val = tensor(false)]; + tensor var_2977 = scatter_along_axis(axis = var_2977_axis_0, data = var_2975, indices = write_indices_13, mode = var_2977_mode_0, updates = k_27, validate_indices = var_2977_validate_indices_0)[name = tensor("op_2977")]; + tensor concat_43 = const()[name = tensor("concat_43"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_44 = const()[name = tensor("concat_44"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_13_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_13_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_58 = const()[name = tensor("shape_58"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_12 = const()[name = tensor("reduce_prod_12"), val = tensor(1048576)]; + tensor range_1d_12_start_0 = const()[name = tensor("range_1d_12_start_0"), val = tensor(0)]; + tensor range_1d_12_step_0 = const()[name = tensor("range_1d_12_step_0"), val = tensor(1)]; + tensor range_1d_12 = range_1d(end = reduce_prod_12, start = range_1d_12_start_0, step = range_1d_12_step_0)[name = tensor("range_1d_12")]; + tensor reshape_60 = reshape(shape = shape_58, x = range_1d_12)[name = tensor("reshape_60")]; + tensor slice_by_index_12 = slice_by_index(begin = concat_43, begin_mask = new_cache_13_internal_tensor_assign_1_begin_mask_0, end = concat_44, end_mask = new_cache_13_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_13_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_13_internal_tensor_assign_1_stride_0, x = reshape_60)[name = tensor("slice_by_index_12")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([-1])]; + tensor reshape_61 = reshape(shape = reshape_61_shape_0, x = slice_by_index_12)[name = tensor("reshape_61")]; + tensor reshape_62_shape_0 = const()[name = tensor("reshape_62_shape_0"), val = tensor([-1])]; + tensor reshape_62 = reshape(shape = reshape_62_shape_0, x = var_2977)[name = tensor("reshape_62")]; + tensor reshape_63_shape_0 = const()[name = tensor("reshape_63_shape_0"), val = tensor([-1])]; + tensor reshape_63 = reshape(shape = reshape_63_shape_0, x = cache6)[name = tensor("reshape_63")]; + tensor scatter_12_mode_0 = const()[name = tensor("scatter_12_mode_0"), val = tensor("update")]; + tensor scatter_12_axis_0 = const()[name = tensor("scatter_12_axis_0"), val = tensor(0)]; + tensor scatter_12_validate_indices_0 = const()[name = tensor("scatter_12_validate_indices_0"), val = tensor(false)]; + tensor scatter_12 = scatter(axis = scatter_12_axis_0, data = reshape_63, indices = reshape_61, mode = scatter_12_mode_0, updates = reshape_62, validate_indices = scatter_12_validate_indices_0)[name = tensor("scatter_12")]; + tensor reshape_64 = reshape(shape = shape_58, x = scatter_12)[name = tensor("reshape_64")]; + tensor var_2985_begin_0 = const()[name = tensor("op_2985_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2985_end_0 = const()[name = tensor("op_2985_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2985_end_mask_0 = const()[name = tensor("op_2985_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2985_squeeze_mask_0 = const()[name = tensor("op_2985_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2985 = slice_by_index(begin = var_2985_begin_0, end = var_2985_end_0, end_mask = var_2985_end_mask_0, squeeze_mask = var_2985_squeeze_mask_0, x = reshape_64)[name = tensor("op_2985")]; + tensor var_2987_axis_0 = const()[name = tensor("op_2987_axis_0"), val = tensor(1)]; + tensor var_2987_mode_0 = const()[name = tensor("op_2987_mode_0"), val = tensor("update")]; + tensor var_2987_validate_indices_0 = const()[name = tensor("op_2987_validate_indices_0"), val = tensor(false)]; + tensor var_2987 = scatter_along_axis(axis = var_2987_axis_0, data = var_2985, indices = write_indices_13, mode = var_2987_mode_0, updates = v_13, validate_indices = var_2987_validate_indices_0)[name = tensor("op_2987")]; + tensor concat_45 = const()[name = tensor("concat_45"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_46 = const()[name = tensor("concat_46"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_13_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_13_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_59 = const()[name = tensor("shape_59"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_13 = const()[name = tensor("reduce_prod_13"), val = tensor(1048576)]; + tensor range_1d_13_start_0 = const()[name = tensor("range_1d_13_start_0"), val = tensor(0)]; + tensor range_1d_13_step_0 = const()[name = tensor("range_1d_13_step_0"), val = tensor(1)]; + tensor range_1d_13 = range_1d(end = reduce_prod_13, start = range_1d_13_start_0, step = range_1d_13_step_0)[name = tensor("range_1d_13")]; + tensor reshape_65 = reshape(shape = shape_59, x = range_1d_13)[name = tensor("reshape_65")]; + tensor slice_by_index_13 = slice_by_index(begin = concat_45, begin_mask = new_cache_13_internal_tensor_assign_2_begin_mask_0, end = concat_46, end_mask = new_cache_13_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_13_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_13_internal_tensor_assign_2_stride_0, x = reshape_65)[name = tensor("slice_by_index_13")]; + tensor reshape_66_shape_0 = const()[name = tensor("reshape_66_shape_0"), val = tensor([-1])]; + tensor reshape_66 = reshape(shape = reshape_66_shape_0, x = slice_by_index_13)[name = tensor("reshape_66")]; + tensor reshape_67_shape_0 = const()[name = tensor("reshape_67_shape_0"), val = tensor([-1])]; + tensor reshape_67 = reshape(shape = reshape_67_shape_0, x = var_2987)[name = tensor("reshape_67")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([-1])]; + tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = reshape_64)[name = tensor("reshape_68")]; + tensor scatter_13_mode_0 = const()[name = tensor("scatter_13_mode_0"), val = tensor("update")]; + tensor scatter_13_axis_0 = const()[name = tensor("scatter_13_axis_0"), val = tensor(0)]; + tensor scatter_13_validate_indices_0 = const()[name = tensor("scatter_13_validate_indices_0"), val = tensor(false)]; + tensor scatter_13 = scatter(axis = scatter_13_axis_0, data = reshape_68, indices = reshape_66, mode = scatter_13_mode_0, updates = reshape_67, validate_indices = scatter_13_validate_indices_0)[name = tensor("scatter_13")]; + tensor new_cache_13_internal_tensor_assign_2 = reshape(shape = shape_59, x = scatter_13)[name = tensor("reshape_69")]; + tensor keys_37_begin_0 = const()[name = tensor("keys_37_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_37_end_0 = const()[name = tensor("keys_37_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_37_end_mask_0 = const()[name = tensor("keys_37_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_37_squeeze_mask_0 = const()[name = tensor("keys_37_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_37 = slice_by_index(begin = keys_37_begin_0, end = keys_37_end_0, end_mask = keys_37_end_mask_0, squeeze_mask = keys_37_squeeze_mask_0, x = new_cache_13_internal_tensor_assign_2)[name = tensor("keys_37")]; + tensor values_37_begin_0 = const()[name = tensor("values_37_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_37_end_0 = const()[name = tensor("values_37_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_37_end_mask_0 = const()[name = tensor("values_37_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_37_squeeze_mask_0 = const()[name = tensor("values_37_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_37 = slice_by_index(begin = values_37_begin_0, end = values_37_end_0, end_mask = values_37_end_mask_0, squeeze_mask = values_37_squeeze_mask_0, x = new_cache_13_internal_tensor_assign_2)[name = tensor("values_37")]; + tensor var_2999 = not_equal(x = keys_37, y = keys_37)[name = tensor("op_2999")]; + tensor keys_39 = select(a = var_491, b = keys_37, cond = var_2999)[name = tensor("keys_39")]; + tensor var_3007 = not_equal(x = values_37, y = values_37)[name = tensor("op_3007")]; + tensor values_39 = select(a = var_491, b = values_37, cond = var_3007)[name = tensor("values_39")]; + tensor var_3031 = const()[name = tensor("op_3031"), val = tensor([0, 2, 1, 3])]; + tensor var_3044 = const()[name = tensor("op_3044"), val = tensor([1, 1, 1])]; + tensor var_3045 = reshape(shape = var_3044, x = position6)[name = tensor("op_3045")]; + tensor var_3062 = const()[name = tensor("op_3062"), val = tensor(0x1p+0)]; + tensor valid_len_13 = add(x = var_3045, y = var_3062)[name = tensor("valid_len_13")]; + tensor valid_mask_13 = less(x = k_positions_1_promoted, y = valid_len_13)[name = tensor("valid_mask_13")]; + tensor causal_mask_13 = less_equal(x = k_positions_1_promoted, y = var_3045)[name = tensor("causal_mask_13")]; + tensor attn_mask_25 = logical_and(x = valid_mask_13, y = causal_mask_13)[name = tensor("attn_mask_25")]; + tensor attn_mask_27_axes_0 = const()[name = tensor("attn_mask_27_axes_0"), val = tensor([1])]; + tensor attn_mask_27 = expand_dims(axes = attn_mask_27_axes_0, x = attn_mask_25)[name = tensor("attn_mask_27")]; + tensor var_3074 = const()[name = tensor("op_3074"), val = tensor([0x1.fffe5cp-4])]; + tensor var_3080_transpose_x_0 = const()[name = tensor("op_3080_transpose_x_0"), val = tensor(false)]; + tensor var_3080_transpose_y_0 = const()[name = tensor("op_3080_transpose_y_0"), val = tensor(false)]; + tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = keys_39)[name = tensor("transpose_180")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = q_39)[name = tensor("transpose_181")]; + tensor var_3080 = matmul(transpose_x = var_3080_transpose_x_0, transpose_y = var_3080_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor("op_3080")]; + tensor attn_weights_37 = mul(x = var_3080, y = var_3074)[name = tensor("attn_weights_37")]; + tensor var_3082 = logical_not(x = attn_mask_27)[name = tensor("op_3082")]; + tensor var_3083 = const()[name = tensor("op_3083"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_39 = select(a = var_3083, b = attn_weights_37, cond = var_3082)[name = tensor("attn_weights_39")]; + tensor var_3085 = const()[name = tensor("op_3085"), val = tensor(-1)]; + tensor attn_weights_41 = softmax(axis = var_3085, x = attn_weights_39)[name = tensor("attn_weights_41")]; + tensor attn_output_13_transpose_x_0 = const()[name = tensor("attn_output_13_transpose_x_0"), val = tensor(false)]; + tensor attn_output_13_transpose_y_0 = const()[name = tensor("attn_output_13_transpose_y_0"), val = tensor(false)]; + tensor values_41 = transpose(perm = var_3031, x = values_39)[name = tensor("transpose_182")]; + tensor attn_output_13 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = attn_weights_41, y = values_41)[name = tensor("attn_output_13")]; + tensor var_3093 = const()[name = tensor("op_3093"), val = tensor([0, 2, 1, 3])]; + tensor var_3096 = const()[name = tensor("op_3096"), val = tensor([1, 1, 1024])]; + tensor var_3094 = transpose(perm = var_3093, x = attn_output_13)[name = tensor("transpose_179")]; + tensor input_63 = reshape(shape = var_3096, x = var_3094)[name = tensor("input_63")]; + tensor attn_out_13 = linear(bias = linear_1_bias_0, weight = attn6_out_proj_weight, x = input_63)[name = tensor("linear_25")]; + tensor var_3102 = const()[name = tensor("op_3102"), val = tensor(0x1p+0)]; + tensor var_3103 = add(x = position6, y = var_3102)[name = tensor("op_3103")]; + tensor input_65 = add(x = input_61, y = attn_out_13)[name = tensor("input_65")]; + tensor var_3107 = const()[name = tensor("op_3107"), val = tensor(0x1.4f8b58p-17)]; + tensor input_67_axes_0 = const()[name = tensor("input_67_axes_0"), val = tensor([-1])]; + tensor input_67 = layer_norm(axes = input_67_axes_0, beta = norm6_2_bias, epsilon = var_3107, gamma = norm6_2_weight, x = input_65)[name = tensor("input_67")]; + tensor var_3115 = linear(bias = linear_2_bias_0, weight = linear6_1_weight, x = input_67)[name = tensor("linear_26")]; + tensor input_69_mode_0 = const()[name = tensor("input_69_mode_0"), val = tensor("EXACT")]; + tensor input_69 = gelu(mode = input_69_mode_0, x = var_3115)[name = tensor("input_69")]; + tensor ffn_out_13 = linear(bias = linear_1_bias_0, weight = linear6_2_weight, x = input_69)[name = tensor("linear_27")]; + tensor input_71 = add(x = input_65, y = ffn_out_13)[name = tensor("input_71")]; + tensor var_3124 = const()[name = tensor("op_3124"), val = tensor(0x1.4f8b58p-17)]; + tensor x_15_axes_0 = const()[name = tensor("x_15_axes_0"), val = tensor([-1])]; + tensor x_15 = layer_norm(axes = x_15_axes_0, beta = norm7_1_bias, epsilon = var_3124, gamma = norm7_1_weight, x = input_71)[name = tensor("x_15")]; + tensor var_3156 = linear(bias = linear_0_bias_0, weight = attn7_in_proj_weight, x = x_15)[name = tensor("linear_28")]; + tensor var_3160 = const()[name = tensor("op_3160"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_15 = reshape(shape = var_3160, x = var_3156)[name = tensor("qkv_15")]; + tensor q_43_begin_0 = const()[name = tensor("q_43_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_43_end_0 = const()[name = tensor("q_43_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_43_end_mask_0 = const()[name = tensor("q_43_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_43_squeeze_mask_0 = const()[name = tensor("q_43_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_43 = slice_by_index(begin = q_43_begin_0, end = q_43_end_0, end_mask = q_43_end_mask_0, squeeze_mask = q_43_squeeze_mask_0, x = qkv_15)[name = tensor("q_43")]; + tensor k_29_begin_0 = const()[name = tensor("k_29_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_29_end_0 = const()[name = tensor("k_29_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_29_end_mask_0 = const()[name = tensor("k_29_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_29_squeeze_mask_0 = const()[name = tensor("k_29_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_29 = slice_by_index(begin = k_29_begin_0, end = k_29_end_0, end_mask = k_29_end_mask_0, squeeze_mask = k_29_squeeze_mask_0, x = qkv_15)[name = tensor("k_29")]; + tensor v_15_begin_0 = const()[name = tensor("v_15_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_15_end_0 = const()[name = tensor("v_15_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_15_end_mask_0 = const()[name = tensor("v_15_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_15_squeeze_mask_0 = const()[name = tensor("v_15_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_15 = slice_by_index(begin = v_15_begin_0, end = v_15_end_0, end_mask = v_15_end_mask_0, squeeze_mask = v_15_squeeze_mask_0, x = qkv_15)[name = tensor("v_15")]; + tensor freqs_15 = const()[name = tensor("freqs_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172741504)))]; + tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([1, 1, 1, 1])]; + tensor ts_47 = reshape(shape = var_3264, x = position7)[name = tensor("ts_47")]; + tensor var_3268 = const()[name = tensor("op_3268"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_15 = reshape(shape = var_3268, x = q_43)[name = tensor("q_complex_15")]; + tensor var_3272 = const()[name = tensor("op_3272"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_15 = reshape(shape = var_3272, x = k_29)[name = tensor("k_complex_15")]; + tensor var_3276_begin_0 = const()[name = tensor("op_3276_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3276_end_0 = const()[name = tensor("op_3276_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3276_end_mask_0 = const()[name = tensor("op_3276_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3276_squeeze_mask_0 = const()[name = tensor("op_3276_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3276 = slice_by_index(begin = var_3276_begin_0, end = var_3276_end_0, end_mask = var_3276_end_mask_0, squeeze_mask = var_3276_squeeze_mask_0, x = q_complex_15)[name = tensor("op_3276")]; + tensor var_3284_begin_0 = const()[name = tensor("op_3284_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3284_end_0 = const()[name = tensor("op_3284_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3284_end_mask_0 = const()[name = tensor("op_3284_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3284_squeeze_mask_0 = const()[name = tensor("op_3284_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3284 = slice_by_index(begin = var_3284_begin_0, end = var_3284_end_0, end_mask = var_3284_end_mask_0, squeeze_mask = var_3284_squeeze_mask_0, x = q_complex_15)[name = tensor("op_3284")]; + tensor var_3292_begin_0 = const()[name = tensor("op_3292_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3292_end_0 = const()[name = tensor("op_3292_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3292_end_mask_0 = const()[name = tensor("op_3292_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3292_squeeze_mask_0 = const()[name = tensor("op_3292_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3292 = slice_by_index(begin = var_3292_begin_0, end = var_3292_end_0, end_mask = var_3292_end_mask_0, squeeze_mask = var_3292_squeeze_mask_0, x = k_complex_15)[name = tensor("op_3292")]; + tensor var_3300_begin_0 = const()[name = tensor("op_3300_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3300_end_0 = const()[name = tensor("op_3300_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3300_end_mask_0 = const()[name = tensor("op_3300_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3300_squeeze_mask_0 = const()[name = tensor("op_3300_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3300 = slice_by_index(begin = var_3300_begin_0, end = var_3300_end_0, end_mask = var_3300_end_mask_0, squeeze_mask = var_3300_squeeze_mask_0, x = k_complex_15)[name = tensor("op_3300")]; + tensor var_3306 = mul(x = freqs_15, y = ts_47)[name = tensor("op_3306")]; + tensor rotr_15 = cos(x = var_3306)[name = tensor("rotr_15")]; + tensor roti_15 = sin(x = var_3306)[name = tensor("roti_15")]; + tensor var_3310 = mul(x = var_3276, y = rotr_15)[name = tensor("op_3310")]; + tensor var_3311 = mul(x = var_3284, y = roti_15)[name = tensor("op_3311")]; + tensor qor_29 = sub(x = var_3310, y = var_3311)[name = tensor("qor_29")]; + tensor var_3314 = mul(x = var_3276, y = roti_15)[name = tensor("op_3314")]; + tensor var_3315 = mul(x = var_3284, y = rotr_15)[name = tensor("op_3315")]; + tensor qoi_29 = add(x = var_3314, y = var_3315)[name = tensor("qoi_29")]; + tensor var_3318 = mul(x = var_3292, y = rotr_15)[name = tensor("op_3318")]; + tensor var_3319 = mul(x = var_3300, y = roti_15)[name = tensor("op_3319")]; + tensor kor_29 = sub(x = var_3318, y = var_3319)[name = tensor("kor_29")]; + tensor var_3322 = mul(x = var_3292, y = roti_15)[name = tensor("op_3322")]; + tensor var_3323 = mul(x = var_3300, y = rotr_15)[name = tensor("op_3323")]; + tensor koi_29 = add(x = var_3322, y = var_3323)[name = tensor("koi_29")]; + tensor qo_15_axis_0 = const()[name = tensor("qo_15_axis_0"), val = tensor(-1)]; + tensor qo_15 = stack(axis = qo_15_axis_0, values = (qor_29, qoi_29))[name = tensor("qo_15")]; + tensor ko_15_axis_0 = const()[name = tensor("ko_15_axis_0"), val = tensor(-1)]; + tensor ko_15 = stack(axis = ko_15_axis_0, values = (kor_29, koi_29))[name = tensor("ko_15")]; + tensor var_3352 = const()[name = tensor("op_3352"), val = tensor([1, 1, 16, 64])]; + tensor q_45 = reshape(shape = var_3352, x = qo_15)[name = tensor("q_45")]; + tensor var_3354 = const()[name = tensor("op_3354"), val = tensor([1, 1, 16, 64])]; + tensor k_31 = reshape(shape = var_3354, x = ko_15)[name = tensor("k_31")]; + tensor _inversed_3376_y_0 = const()[name = tensor("_inversed_3376_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_3376 = mul(x = ts_47, y = _inversed_3376_y_0)[name = tensor("_inversed_3376")]; + tensor var_3377 = floor(x = _inversed_3376)[name = tensor("op_3377")]; + tensor var_3378 = const()[name = tensor("op_3378"), val = tensor(0x1p+9)]; + tensor var_3379 = mul(x = var_3377, y = var_3378)[name = tensor("op_3379")]; + tensor write_indices_float_31 = sub(x = ts_47, y = var_3379)[name = tensor("write_indices_float_31")]; + tensor var_3386_dtype_0 = const()[name = tensor("op_3386_dtype_0"), val = tensor("int32")]; + tensor write_indices_15_reps_0 = const()[name = tensor("write_indices_15_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_3386 = cast(dtype = var_3386_dtype_0, x = write_indices_float_31)[name = tensor("cast_439")]; + tensor write_indices_15 = tile(reps = write_indices_15_reps_0, x = var_3386)[name = tensor("write_indices_15")]; + tensor var_3394_begin_0 = const()[name = tensor("op_3394_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3394_end_0 = const()[name = tensor("op_3394_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_3394_end_mask_0 = const()[name = tensor("op_3394_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3394_squeeze_mask_0 = const()[name = tensor("op_3394_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3394 = slice_by_index(begin = var_3394_begin_0, end = var_3394_end_0, end_mask = var_3394_end_mask_0, squeeze_mask = var_3394_squeeze_mask_0, x = cache7)[name = tensor("op_3394")]; + tensor var_3396_axis_0 = const()[name = tensor("op_3396_axis_0"), val = tensor(1)]; + tensor var_3396_mode_0 = const()[name = tensor("op_3396_mode_0"), val = tensor("update")]; + tensor var_3396_validate_indices_0 = const()[name = tensor("op_3396_validate_indices_0"), val = tensor(false)]; + tensor var_3396 = scatter_along_axis(axis = var_3396_axis_0, data = var_3394, indices = write_indices_15, mode = var_3396_mode_0, updates = k_31, validate_indices = var_3396_validate_indices_0)[name = tensor("op_3396")]; + tensor concat_50 = const()[name = tensor("concat_50"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_51 = const()[name = tensor("concat_51"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_15_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_15_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_60 = const()[name = tensor("shape_60"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_14 = const()[name = tensor("reduce_prod_14"), val = tensor(1048576)]; + tensor range_1d_14_start_0 = const()[name = tensor("range_1d_14_start_0"), val = tensor(0)]; + tensor range_1d_14_step_0 = const()[name = tensor("range_1d_14_step_0"), val = tensor(1)]; + tensor range_1d_14 = range_1d(end = reduce_prod_14, start = range_1d_14_start_0, step = range_1d_14_step_0)[name = tensor("range_1d_14")]; + tensor reshape_70 = reshape(shape = shape_60, x = range_1d_14)[name = tensor("reshape_70")]; + tensor slice_by_index_14 = slice_by_index(begin = concat_50, begin_mask = new_cache_15_internal_tensor_assign_1_begin_mask_0, end = concat_51, end_mask = new_cache_15_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_15_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_15_internal_tensor_assign_1_stride_0, x = reshape_70)[name = tensor("slice_by_index_14")]; + tensor reshape_71_shape_0 = const()[name = tensor("reshape_71_shape_0"), val = tensor([-1])]; + tensor reshape_71 = reshape(shape = reshape_71_shape_0, x = slice_by_index_14)[name = tensor("reshape_71")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([-1])]; + tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = var_3396)[name = tensor("reshape_72")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([-1])]; + tensor reshape_73 = reshape(shape = reshape_73_shape_0, x = cache7)[name = tensor("reshape_73")]; + tensor scatter_14_mode_0 = const()[name = tensor("scatter_14_mode_0"), val = tensor("update")]; + tensor scatter_14_axis_0 = const()[name = tensor("scatter_14_axis_0"), val = tensor(0)]; + tensor scatter_14_validate_indices_0 = const()[name = tensor("scatter_14_validate_indices_0"), val = tensor(false)]; + tensor scatter_14 = scatter(axis = scatter_14_axis_0, data = reshape_73, indices = reshape_71, mode = scatter_14_mode_0, updates = reshape_72, validate_indices = scatter_14_validate_indices_0)[name = tensor("scatter_14")]; + tensor reshape_74 = reshape(shape = shape_60, x = scatter_14)[name = tensor("reshape_74")]; + tensor var_3404_begin_0 = const()[name = tensor("op_3404_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_3404_end_0 = const()[name = tensor("op_3404_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_3404_end_mask_0 = const()[name = tensor("op_3404_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3404_squeeze_mask_0 = const()[name = tensor("op_3404_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3404 = slice_by_index(begin = var_3404_begin_0, end = var_3404_end_0, end_mask = var_3404_end_mask_0, squeeze_mask = var_3404_squeeze_mask_0, x = reshape_74)[name = tensor("op_3404")]; + tensor var_3406_axis_0 = const()[name = tensor("op_3406_axis_0"), val = tensor(1)]; + tensor var_3406_mode_0 = const()[name = tensor("op_3406_mode_0"), val = tensor("update")]; + tensor var_3406_validate_indices_0 = const()[name = tensor("op_3406_validate_indices_0"), val = tensor(false)]; + tensor var_3406 = scatter_along_axis(axis = var_3406_axis_0, data = var_3404, indices = write_indices_15, mode = var_3406_mode_0, updates = v_15, validate_indices = var_3406_validate_indices_0)[name = tensor("op_3406")]; + tensor concat_52 = const()[name = tensor("concat_52"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_53 = const()[name = tensor("concat_53"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_15_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_15_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_61 = const()[name = tensor("shape_61"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_15 = const()[name = tensor("reduce_prod_15"), val = tensor(1048576)]; + tensor range_1d_15_start_0 = const()[name = tensor("range_1d_15_start_0"), val = tensor(0)]; + tensor range_1d_15_step_0 = const()[name = tensor("range_1d_15_step_0"), val = tensor(1)]; + tensor range_1d_15 = range_1d(end = reduce_prod_15, start = range_1d_15_start_0, step = range_1d_15_step_0)[name = tensor("range_1d_15")]; + tensor reshape_75 = reshape(shape = shape_61, x = range_1d_15)[name = tensor("reshape_75")]; + tensor slice_by_index_15 = slice_by_index(begin = concat_52, begin_mask = new_cache_15_internal_tensor_assign_2_begin_mask_0, end = concat_53, end_mask = new_cache_15_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_15_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_15_internal_tensor_assign_2_stride_0, x = reshape_75)[name = tensor("slice_by_index_15")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([-1])]; + tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = slice_by_index_15)[name = tensor("reshape_76")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([-1])]; + tensor reshape_77 = reshape(shape = reshape_77_shape_0, x = var_3406)[name = tensor("reshape_77")]; + tensor reshape_78_shape_0 = const()[name = tensor("reshape_78_shape_0"), val = tensor([-1])]; + tensor reshape_78 = reshape(shape = reshape_78_shape_0, x = reshape_74)[name = tensor("reshape_78")]; + tensor scatter_15_mode_0 = const()[name = tensor("scatter_15_mode_0"), val = tensor("update")]; + tensor scatter_15_axis_0 = const()[name = tensor("scatter_15_axis_0"), val = tensor(0)]; + tensor scatter_15_validate_indices_0 = const()[name = tensor("scatter_15_validate_indices_0"), val = tensor(false)]; + tensor scatter_15 = scatter(axis = scatter_15_axis_0, data = reshape_78, indices = reshape_76, mode = scatter_15_mode_0, updates = reshape_77, validate_indices = scatter_15_validate_indices_0)[name = tensor("scatter_15")]; + tensor new_cache_15_internal_tensor_assign_2 = reshape(shape = shape_61, x = scatter_15)[name = tensor("reshape_79")]; + tensor keys_43_begin_0 = const()[name = tensor("keys_43_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_43_end_0 = const()[name = tensor("keys_43_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_43_end_mask_0 = const()[name = tensor("keys_43_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_43_squeeze_mask_0 = const()[name = tensor("keys_43_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_43 = slice_by_index(begin = keys_43_begin_0, end = keys_43_end_0, end_mask = keys_43_end_mask_0, squeeze_mask = keys_43_squeeze_mask_0, x = new_cache_15_internal_tensor_assign_2)[name = tensor("keys_43")]; + tensor values_43_begin_0 = const()[name = tensor("values_43_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_43_end_0 = const()[name = tensor("values_43_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_43_end_mask_0 = const()[name = tensor("values_43_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_43_squeeze_mask_0 = const()[name = tensor("values_43_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_43 = slice_by_index(begin = values_43_begin_0, end = values_43_end_0, end_mask = values_43_end_mask_0, squeeze_mask = values_43_squeeze_mask_0, x = new_cache_15_internal_tensor_assign_2)[name = tensor("values_43")]; + tensor var_3418 = not_equal(x = keys_43, y = keys_43)[name = tensor("op_3418")]; + tensor keys_45 = select(a = var_491, b = keys_43, cond = var_3418)[name = tensor("keys_45")]; + tensor var_3426 = not_equal(x = values_43, y = values_43)[name = tensor("op_3426")]; + tensor values_45 = select(a = var_491, b = values_43, cond = var_3426)[name = tensor("values_45")]; + tensor var_3450 = const()[name = tensor("op_3450"), val = tensor([0, 2, 1, 3])]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 1, 1])]; + tensor var_3464 = reshape(shape = var_3463, x = position7)[name = tensor("op_3464")]; + tensor var_3481 = const()[name = tensor("op_3481"), val = tensor(0x1p+0)]; + tensor valid_len_15 = add(x = var_3464, y = var_3481)[name = tensor("valid_len_15")]; + tensor valid_mask_15 = less(x = k_positions_1_promoted, y = valid_len_15)[name = tensor("valid_mask_15")]; + tensor causal_mask_15 = less_equal(x = k_positions_1_promoted, y = var_3464)[name = tensor("causal_mask_15")]; + tensor attn_mask_29 = logical_and(x = valid_mask_15, y = causal_mask_15)[name = tensor("attn_mask_29")]; + tensor attn_mask_31_axes_0 = const()[name = tensor("attn_mask_31_axes_0"), val = tensor([1])]; + tensor attn_mask_31 = expand_dims(axes = attn_mask_31_axes_0, x = attn_mask_29)[name = tensor("attn_mask_31")]; + tensor var_3493 = const()[name = tensor("op_3493"), val = tensor([0x1.fffe5cp-4])]; + tensor var_3499_transpose_x_0 = const()[name = tensor("op_3499_transpose_x_0"), val = tensor(false)]; + tensor var_3499_transpose_y_0 = const()[name = tensor("op_3499_transpose_y_0"), val = tensor(false)]; + tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = keys_45)[name = tensor("transpose_176")]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = q_45)[name = tensor("transpose_177")]; + tensor var_3499 = matmul(transpose_x = var_3499_transpose_x_0, transpose_y = var_3499_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor("op_3499")]; + tensor attn_weights_43 = mul(x = var_3499, y = var_3493)[name = tensor("attn_weights_43")]; + tensor var_3501 = logical_not(x = attn_mask_31)[name = tensor("op_3501")]; + tensor var_3502 = const()[name = tensor("op_3502"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_45 = select(a = var_3502, b = attn_weights_43, cond = var_3501)[name = tensor("attn_weights_45")]; + tensor var_3504 = const()[name = tensor("op_3504"), val = tensor(-1)]; + tensor attn_weights_47 = softmax(axis = var_3504, x = attn_weights_45)[name = tensor("attn_weights_47")]; + tensor attn_output_15_transpose_x_0 = const()[name = tensor("attn_output_15_transpose_x_0"), val = tensor(false)]; + tensor attn_output_15_transpose_y_0 = const()[name = tensor("attn_output_15_transpose_y_0"), val = tensor(false)]; + tensor values_47 = transpose(perm = var_3450, x = values_45)[name = tensor("transpose_178")]; + tensor attn_output_15 = matmul(transpose_x = attn_output_15_transpose_x_0, transpose_y = attn_output_15_transpose_y_0, x = attn_weights_47, y = values_47)[name = tensor("attn_output_15")]; + tensor var_3512 = const()[name = tensor("op_3512"), val = tensor([0, 2, 1, 3])]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1, 1, 1024])]; + tensor var_3513 = transpose(perm = var_3512, x = attn_output_15)[name = tensor("transpose_175")]; + tensor input_73 = reshape(shape = var_3515, x = var_3513)[name = tensor("input_73")]; + tensor attn_out_15 = linear(bias = linear_1_bias_0, weight = attn7_out_proj_weight, x = input_73)[name = tensor("linear_29")]; + tensor var_3521 = const()[name = tensor("op_3521"), val = tensor(0x1p+0)]; + tensor var_3522 = add(x = position7, y = var_3521)[name = tensor("op_3522")]; + tensor input_75 = add(x = input_71, y = attn_out_15)[name = tensor("input_75")]; + tensor var_3526 = const()[name = tensor("op_3526"), val = tensor(0x1.4f8b58p-17)]; + tensor input_77_axes_0 = const()[name = tensor("input_77_axes_0"), val = tensor([-1])]; + tensor input_77 = layer_norm(axes = input_77_axes_0, beta = norm7_2_bias, epsilon = var_3526, gamma = norm7_2_weight, x = input_75)[name = tensor("input_77")]; + tensor var_3534 = linear(bias = linear_2_bias_0, weight = linear7_1_weight, x = input_77)[name = tensor("linear_30")]; + tensor input_79_mode_0 = const()[name = tensor("input_79_mode_0"), val = tensor("EXACT")]; + tensor input_79 = gelu(mode = input_79_mode_0, x = var_3534)[name = tensor("input_79")]; + tensor ffn_out_15 = linear(bias = linear_1_bias_0, weight = linear7_2_weight, x = input_79)[name = tensor("linear_31")]; + tensor input_81 = add(x = input_75, y = ffn_out_15)[name = tensor("input_81")]; + tensor var_3543 = const()[name = tensor("op_3543"), val = tensor(0x1.4f8b58p-17)]; + tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; + tensor x_17 = layer_norm(axes = x_17_axes_0, beta = norm8_1_bias, epsilon = var_3543, gamma = norm8_1_weight, x = input_81)[name = tensor("x_17")]; + tensor var_3575 = linear(bias = linear_0_bias_0, weight = attn8_in_proj_weight, x = x_17)[name = tensor("linear_32")]; + tensor var_3579 = const()[name = tensor("op_3579"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_17 = reshape(shape = var_3579, x = var_3575)[name = tensor("qkv_17")]; + tensor q_49_begin_0 = const()[name = tensor("q_49_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_49_end_0 = const()[name = tensor("q_49_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_49_end_mask_0 = const()[name = tensor("q_49_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_49_squeeze_mask_0 = const()[name = tensor("q_49_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_49 = slice_by_index(begin = q_49_begin_0, end = q_49_end_0, end_mask = q_49_end_mask_0, squeeze_mask = q_49_squeeze_mask_0, x = qkv_17)[name = tensor("q_49")]; + tensor k_33_begin_0 = const()[name = tensor("k_33_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_33_end_0 = const()[name = tensor("k_33_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_33_end_mask_0 = const()[name = tensor("k_33_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_33_squeeze_mask_0 = const()[name = tensor("k_33_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_33 = slice_by_index(begin = k_33_begin_0, end = k_33_end_0, end_mask = k_33_end_mask_0, squeeze_mask = k_33_squeeze_mask_0, x = qkv_17)[name = tensor("k_33")]; + tensor v_17_begin_0 = const()[name = tensor("v_17_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_17_end_0 = const()[name = tensor("v_17_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_17_end_mask_0 = const()[name = tensor("v_17_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_17_squeeze_mask_0 = const()[name = tensor("v_17_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_17 = slice_by_index(begin = v_17_begin_0, end = v_17_end_0, end_mask = v_17_end_mask_0, squeeze_mask = v_17_squeeze_mask_0, x = qkv_17)[name = tensor("v_17")]; + tensor freqs_17 = const()[name = tensor("freqs_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172741696)))]; + tensor var_3683 = const()[name = tensor("op_3683"), val = tensor([1, 1, 1, 1])]; + tensor ts_53 = reshape(shape = var_3683, x = position8)[name = tensor("ts_53")]; + tensor var_3687 = const()[name = tensor("op_3687"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_17 = reshape(shape = var_3687, x = q_49)[name = tensor("q_complex_17")]; + tensor var_3691 = const()[name = tensor("op_3691"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_17 = reshape(shape = var_3691, x = k_33)[name = tensor("k_complex_17")]; + tensor var_3695_begin_0 = const()[name = tensor("op_3695_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3695_end_0 = const()[name = tensor("op_3695_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3695_end_mask_0 = const()[name = tensor("op_3695_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3695_squeeze_mask_0 = const()[name = tensor("op_3695_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3695 = slice_by_index(begin = var_3695_begin_0, end = var_3695_end_0, end_mask = var_3695_end_mask_0, squeeze_mask = var_3695_squeeze_mask_0, x = q_complex_17)[name = tensor("op_3695")]; + tensor var_3703_begin_0 = const()[name = tensor("op_3703_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3703_end_0 = const()[name = tensor("op_3703_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3703_end_mask_0 = const()[name = tensor("op_3703_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3703_squeeze_mask_0 = const()[name = tensor("op_3703_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3703 = slice_by_index(begin = var_3703_begin_0, end = var_3703_end_0, end_mask = var_3703_end_mask_0, squeeze_mask = var_3703_squeeze_mask_0, x = q_complex_17)[name = tensor("op_3703")]; + tensor var_3711_begin_0 = const()[name = tensor("op_3711_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3711_end_0 = const()[name = tensor("op_3711_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3711_end_mask_0 = const()[name = tensor("op_3711_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3711_squeeze_mask_0 = const()[name = tensor("op_3711_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3711 = slice_by_index(begin = var_3711_begin_0, end = var_3711_end_0, end_mask = var_3711_end_mask_0, squeeze_mask = var_3711_squeeze_mask_0, x = k_complex_17)[name = tensor("op_3711")]; + tensor var_3719_begin_0 = const()[name = tensor("op_3719_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3719_end_0 = const()[name = tensor("op_3719_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3719_end_mask_0 = const()[name = tensor("op_3719_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3719_squeeze_mask_0 = const()[name = tensor("op_3719_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3719 = slice_by_index(begin = var_3719_begin_0, end = var_3719_end_0, end_mask = var_3719_end_mask_0, squeeze_mask = var_3719_squeeze_mask_0, x = k_complex_17)[name = tensor("op_3719")]; + tensor var_3725 = mul(x = freqs_17, y = ts_53)[name = tensor("op_3725")]; + tensor rotr_17 = cos(x = var_3725)[name = tensor("rotr_17")]; + tensor roti_17 = sin(x = var_3725)[name = tensor("roti_17")]; + tensor var_3729 = mul(x = var_3695, y = rotr_17)[name = tensor("op_3729")]; + tensor var_3730 = mul(x = var_3703, y = roti_17)[name = tensor("op_3730")]; + tensor qor_33 = sub(x = var_3729, y = var_3730)[name = tensor("qor_33")]; + tensor var_3733 = mul(x = var_3695, y = roti_17)[name = tensor("op_3733")]; + tensor var_3734 = mul(x = var_3703, y = rotr_17)[name = tensor("op_3734")]; + tensor qoi_33 = add(x = var_3733, y = var_3734)[name = tensor("qoi_33")]; + tensor var_3737 = mul(x = var_3711, y = rotr_17)[name = tensor("op_3737")]; + tensor var_3738 = mul(x = var_3719, y = roti_17)[name = tensor("op_3738")]; + tensor kor_33 = sub(x = var_3737, y = var_3738)[name = tensor("kor_33")]; + tensor var_3741 = mul(x = var_3711, y = roti_17)[name = tensor("op_3741")]; + tensor var_3742 = mul(x = var_3719, y = rotr_17)[name = tensor("op_3742")]; + tensor koi_33 = add(x = var_3741, y = var_3742)[name = tensor("koi_33")]; + tensor qo_17_axis_0 = const()[name = tensor("qo_17_axis_0"), val = tensor(-1)]; + tensor qo_17 = stack(axis = qo_17_axis_0, values = (qor_33, qoi_33))[name = tensor("qo_17")]; + tensor ko_17_axis_0 = const()[name = tensor("ko_17_axis_0"), val = tensor(-1)]; + tensor ko_17 = stack(axis = ko_17_axis_0, values = (kor_33, koi_33))[name = tensor("ko_17")]; + tensor var_3771 = const()[name = tensor("op_3771"), val = tensor([1, 1, 16, 64])]; + tensor q_51 = reshape(shape = var_3771, x = qo_17)[name = tensor("q_51")]; + tensor var_3773 = const()[name = tensor("op_3773"), val = tensor([1, 1, 16, 64])]; + tensor k_35 = reshape(shape = var_3773, x = ko_17)[name = tensor("k_35")]; + tensor _inversed_3795_y_0 = const()[name = tensor("_inversed_3795_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_3795 = mul(x = ts_53, y = _inversed_3795_y_0)[name = tensor("_inversed_3795")]; + tensor var_3796 = floor(x = _inversed_3795)[name = tensor("op_3796")]; + tensor var_3797 = const()[name = tensor("op_3797"), val = tensor(0x1p+9)]; + tensor var_3798 = mul(x = var_3796, y = var_3797)[name = tensor("op_3798")]; + tensor write_indices_float_35 = sub(x = ts_53, y = var_3798)[name = tensor("write_indices_float_35")]; + tensor var_3805_dtype_0 = const()[name = tensor("op_3805_dtype_0"), val = tensor("int32")]; + tensor write_indices_17_reps_0 = const()[name = tensor("write_indices_17_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_3805 = cast(dtype = var_3805_dtype_0, x = write_indices_float_35)[name = tensor("cast_438")]; + tensor write_indices_17 = tile(reps = write_indices_17_reps_0, x = var_3805)[name = tensor("write_indices_17")]; + tensor var_3813_begin_0 = const()[name = tensor("op_3813_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3813_end_0 = const()[name = tensor("op_3813_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_3813_end_mask_0 = const()[name = tensor("op_3813_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3813_squeeze_mask_0 = const()[name = tensor("op_3813_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3813 = slice_by_index(begin = var_3813_begin_0, end = var_3813_end_0, end_mask = var_3813_end_mask_0, squeeze_mask = var_3813_squeeze_mask_0, x = cache8)[name = tensor("op_3813")]; + tensor var_3815_axis_0 = const()[name = tensor("op_3815_axis_0"), val = tensor(1)]; + tensor var_3815_mode_0 = const()[name = tensor("op_3815_mode_0"), val = tensor("update")]; + tensor var_3815_validate_indices_0 = const()[name = tensor("op_3815_validate_indices_0"), val = tensor(false)]; + tensor var_3815 = scatter_along_axis(axis = var_3815_axis_0, data = var_3813, indices = write_indices_17, mode = var_3815_mode_0, updates = k_35, validate_indices = var_3815_validate_indices_0)[name = tensor("op_3815")]; + tensor concat_57 = const()[name = tensor("concat_57"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_58 = const()[name = tensor("concat_58"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_17_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_17_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_62 = const()[name = tensor("shape_62"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_16 = const()[name = tensor("reduce_prod_16"), val = tensor(1048576)]; + tensor range_1d_16_start_0 = const()[name = tensor("range_1d_16_start_0"), val = tensor(0)]; + tensor range_1d_16_step_0 = const()[name = tensor("range_1d_16_step_0"), val = tensor(1)]; + tensor range_1d_16 = range_1d(end = reduce_prod_16, start = range_1d_16_start_0, step = range_1d_16_step_0)[name = tensor("range_1d_16")]; + tensor reshape_80 = reshape(shape = shape_62, x = range_1d_16)[name = tensor("reshape_80")]; + tensor slice_by_index_16 = slice_by_index(begin = concat_57, begin_mask = new_cache_17_internal_tensor_assign_1_begin_mask_0, end = concat_58, end_mask = new_cache_17_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_17_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_17_internal_tensor_assign_1_stride_0, x = reshape_80)[name = tensor("slice_by_index_16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([-1])]; + tensor reshape_81 = reshape(shape = reshape_81_shape_0, x = slice_by_index_16)[name = tensor("reshape_81")]; + tensor reshape_82_shape_0 = const()[name = tensor("reshape_82_shape_0"), val = tensor([-1])]; + tensor reshape_82 = reshape(shape = reshape_82_shape_0, x = var_3815)[name = tensor("reshape_82")]; + tensor reshape_83_shape_0 = const()[name = tensor("reshape_83_shape_0"), val = tensor([-1])]; + tensor reshape_83 = reshape(shape = reshape_83_shape_0, x = cache8)[name = tensor("reshape_83")]; + tensor scatter_16_mode_0 = const()[name = tensor("scatter_16_mode_0"), val = tensor("update")]; + tensor scatter_16_axis_0 = const()[name = tensor("scatter_16_axis_0"), val = tensor(0)]; + tensor scatter_16_validate_indices_0 = const()[name = tensor("scatter_16_validate_indices_0"), val = tensor(false)]; + tensor scatter_16 = scatter(axis = scatter_16_axis_0, data = reshape_83, indices = reshape_81, mode = scatter_16_mode_0, updates = reshape_82, validate_indices = scatter_16_validate_indices_0)[name = tensor("scatter_16")]; + tensor reshape_84 = reshape(shape = shape_62, x = scatter_16)[name = tensor("reshape_84")]; + tensor var_3823_begin_0 = const()[name = tensor("op_3823_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_3823_end_0 = const()[name = tensor("op_3823_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_3823_end_mask_0 = const()[name = tensor("op_3823_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3823_squeeze_mask_0 = const()[name = tensor("op_3823_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3823 = slice_by_index(begin = var_3823_begin_0, end = var_3823_end_0, end_mask = var_3823_end_mask_0, squeeze_mask = var_3823_squeeze_mask_0, x = reshape_84)[name = tensor("op_3823")]; + tensor var_3825_axis_0 = const()[name = tensor("op_3825_axis_0"), val = tensor(1)]; + tensor var_3825_mode_0 = const()[name = tensor("op_3825_mode_0"), val = tensor("update")]; + tensor var_3825_validate_indices_0 = const()[name = tensor("op_3825_validate_indices_0"), val = tensor(false)]; + tensor var_3825 = scatter_along_axis(axis = var_3825_axis_0, data = var_3823, indices = write_indices_17, mode = var_3825_mode_0, updates = v_17, validate_indices = var_3825_validate_indices_0)[name = tensor("op_3825")]; + tensor concat_59 = const()[name = tensor("concat_59"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_60 = const()[name = tensor("concat_60"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_17_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_17_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_63 = const()[name = tensor("shape_63"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_17 = const()[name = tensor("reduce_prod_17"), val = tensor(1048576)]; + tensor range_1d_17_start_0 = const()[name = tensor("range_1d_17_start_0"), val = tensor(0)]; + tensor range_1d_17_step_0 = const()[name = tensor("range_1d_17_step_0"), val = tensor(1)]; + tensor range_1d_17 = range_1d(end = reduce_prod_17, start = range_1d_17_start_0, step = range_1d_17_step_0)[name = tensor("range_1d_17")]; + tensor reshape_85 = reshape(shape = shape_63, x = range_1d_17)[name = tensor("reshape_85")]; + tensor slice_by_index_17 = slice_by_index(begin = concat_59, begin_mask = new_cache_17_internal_tensor_assign_2_begin_mask_0, end = concat_60, end_mask = new_cache_17_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_17_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_17_internal_tensor_assign_2_stride_0, x = reshape_85)[name = tensor("slice_by_index_17")]; + tensor reshape_86_shape_0 = const()[name = tensor("reshape_86_shape_0"), val = tensor([-1])]; + tensor reshape_86 = reshape(shape = reshape_86_shape_0, x = slice_by_index_17)[name = tensor("reshape_86")]; + tensor reshape_87_shape_0 = const()[name = tensor("reshape_87_shape_0"), val = tensor([-1])]; + tensor reshape_87 = reshape(shape = reshape_87_shape_0, x = var_3825)[name = tensor("reshape_87")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([-1])]; + tensor reshape_88 = reshape(shape = reshape_88_shape_0, x = reshape_84)[name = tensor("reshape_88")]; + tensor scatter_17_mode_0 = const()[name = tensor("scatter_17_mode_0"), val = tensor("update")]; + tensor scatter_17_axis_0 = const()[name = tensor("scatter_17_axis_0"), val = tensor(0)]; + tensor scatter_17_validate_indices_0 = const()[name = tensor("scatter_17_validate_indices_0"), val = tensor(false)]; + tensor scatter_17 = scatter(axis = scatter_17_axis_0, data = reshape_88, indices = reshape_86, mode = scatter_17_mode_0, updates = reshape_87, validate_indices = scatter_17_validate_indices_0)[name = tensor("scatter_17")]; + tensor new_cache_17_internal_tensor_assign_2 = reshape(shape = shape_63, x = scatter_17)[name = tensor("reshape_89")]; + tensor keys_49_begin_0 = const()[name = tensor("keys_49_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_49_end_0 = const()[name = tensor("keys_49_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_49_end_mask_0 = const()[name = tensor("keys_49_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_49_squeeze_mask_0 = const()[name = tensor("keys_49_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_49 = slice_by_index(begin = keys_49_begin_0, end = keys_49_end_0, end_mask = keys_49_end_mask_0, squeeze_mask = keys_49_squeeze_mask_0, x = new_cache_17_internal_tensor_assign_2)[name = tensor("keys_49")]; + tensor values_49_begin_0 = const()[name = tensor("values_49_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_49_end_0 = const()[name = tensor("values_49_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_49_end_mask_0 = const()[name = tensor("values_49_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_49_squeeze_mask_0 = const()[name = tensor("values_49_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_49 = slice_by_index(begin = values_49_begin_0, end = values_49_end_0, end_mask = values_49_end_mask_0, squeeze_mask = values_49_squeeze_mask_0, x = new_cache_17_internal_tensor_assign_2)[name = tensor("values_49")]; + tensor var_3837 = not_equal(x = keys_49, y = keys_49)[name = tensor("op_3837")]; + tensor keys_51 = select(a = var_491, b = keys_49, cond = var_3837)[name = tensor("keys_51")]; + tensor var_3845 = not_equal(x = values_49, y = values_49)[name = tensor("op_3845")]; + tensor values_51 = select(a = var_491, b = values_49, cond = var_3845)[name = tensor("values_51")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([0, 2, 1, 3])]; + tensor var_3882 = const()[name = tensor("op_3882"), val = tensor([1, 1, 1])]; + tensor var_3883 = reshape(shape = var_3882, x = position8)[name = tensor("op_3883")]; + tensor var_3900 = const()[name = tensor("op_3900"), val = tensor(0x1p+0)]; + tensor valid_len_17 = add(x = var_3883, y = var_3900)[name = tensor("valid_len_17")]; + tensor valid_mask_17 = less(x = k_positions_1_promoted, y = valid_len_17)[name = tensor("valid_mask_17")]; + tensor causal_mask_17 = less_equal(x = k_positions_1_promoted, y = var_3883)[name = tensor("causal_mask_17")]; + tensor attn_mask_33 = logical_and(x = valid_mask_17, y = causal_mask_17)[name = tensor("attn_mask_33")]; + tensor attn_mask_35_axes_0 = const()[name = tensor("attn_mask_35_axes_0"), val = tensor([1])]; + tensor attn_mask_35 = expand_dims(axes = attn_mask_35_axes_0, x = attn_mask_33)[name = tensor("attn_mask_35")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([0x1.fffe5cp-4])]; + tensor var_3918_transpose_x_0 = const()[name = tensor("op_3918_transpose_x_0"), val = tensor(false)]; + tensor var_3918_transpose_y_0 = const()[name = tensor("op_3918_transpose_y_0"), val = tensor(false)]; + tensor transpose_85_perm_0 = const()[name = tensor("transpose_85_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_86_perm_0 = const()[name = tensor("transpose_86_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = keys_51)[name = tensor("transpose_172")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = q_51)[name = tensor("transpose_173")]; + tensor var_3918 = matmul(transpose_x = var_3918_transpose_x_0, transpose_y = var_3918_transpose_y_0, x = transpose_85, y = transpose_86)[name = tensor("op_3918")]; + tensor attn_weights_49 = mul(x = var_3918, y = var_3912)[name = tensor("attn_weights_49")]; + tensor var_3920 = logical_not(x = attn_mask_35)[name = tensor("op_3920")]; + tensor var_3921 = const()[name = tensor("op_3921"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_51 = select(a = var_3921, b = attn_weights_49, cond = var_3920)[name = tensor("attn_weights_51")]; + tensor var_3923 = const()[name = tensor("op_3923"), val = tensor(-1)]; + tensor attn_weights_53 = softmax(axis = var_3923, x = attn_weights_51)[name = tensor("attn_weights_53")]; + tensor attn_output_17_transpose_x_0 = const()[name = tensor("attn_output_17_transpose_x_0"), val = tensor(false)]; + tensor attn_output_17_transpose_y_0 = const()[name = tensor("attn_output_17_transpose_y_0"), val = tensor(false)]; + tensor values_53 = transpose(perm = var_3869, x = values_51)[name = tensor("transpose_174")]; + tensor attn_output_17 = matmul(transpose_x = attn_output_17_transpose_x_0, transpose_y = attn_output_17_transpose_y_0, x = attn_weights_53, y = values_53)[name = tensor("attn_output_17")]; + tensor var_3931 = const()[name = tensor("op_3931"), val = tensor([0, 2, 1, 3])]; + tensor var_3934 = const()[name = tensor("op_3934"), val = tensor([1, 1, 1024])]; + tensor var_3932 = transpose(perm = var_3931, x = attn_output_17)[name = tensor("transpose_171")]; + tensor input_83 = reshape(shape = var_3934, x = var_3932)[name = tensor("input_83")]; + tensor attn_out_17 = linear(bias = linear_1_bias_0, weight = attn8_out_proj_weight, x = input_83)[name = tensor("linear_33")]; + tensor var_3940 = const()[name = tensor("op_3940"), val = tensor(0x1p+0)]; + tensor var_3941 = add(x = position8, y = var_3940)[name = tensor("op_3941")]; + tensor input_85 = add(x = input_81, y = attn_out_17)[name = tensor("input_85")]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(0x1.4f8b58p-17)]; + tensor input_87_axes_0 = const()[name = tensor("input_87_axes_0"), val = tensor([-1])]; + tensor input_87 = layer_norm(axes = input_87_axes_0, beta = norm8_2_bias, epsilon = var_3945, gamma = norm8_2_weight, x = input_85)[name = tensor("input_87")]; + tensor var_3953 = linear(bias = linear_2_bias_0, weight = linear8_1_weight, x = input_87)[name = tensor("linear_34")]; + tensor input_89_mode_0 = const()[name = tensor("input_89_mode_0"), val = tensor("EXACT")]; + tensor input_89 = gelu(mode = input_89_mode_0, x = var_3953)[name = tensor("input_89")]; + tensor ffn_out_17 = linear(bias = linear_1_bias_0, weight = linear8_2_weight, x = input_89)[name = tensor("linear_35")]; + tensor input_91 = add(x = input_85, y = ffn_out_17)[name = tensor("input_91")]; + tensor var_3962 = const()[name = tensor("op_3962"), val = tensor(0x1.4f8b58p-17)]; + tensor x_19_axes_0 = const()[name = tensor("x_19_axes_0"), val = tensor([-1])]; + tensor x_19 = layer_norm(axes = x_19_axes_0, beta = norm9_1_bias, epsilon = var_3962, gamma = norm9_1_weight, x = input_91)[name = tensor("x_19")]; + tensor var_3994 = linear(bias = linear_0_bias_0, weight = attn9_in_proj_weight, x = x_19)[name = tensor("linear_36")]; + tensor var_3998 = const()[name = tensor("op_3998"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_19 = reshape(shape = var_3998, x = var_3994)[name = tensor("qkv_19")]; + tensor q_55_begin_0 = const()[name = tensor("q_55_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_55_end_0 = const()[name = tensor("q_55_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_55_end_mask_0 = const()[name = tensor("q_55_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_55_squeeze_mask_0 = const()[name = tensor("q_55_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_55 = slice_by_index(begin = q_55_begin_0, end = q_55_end_0, end_mask = q_55_end_mask_0, squeeze_mask = q_55_squeeze_mask_0, x = qkv_19)[name = tensor("q_55")]; + tensor k_37_begin_0 = const()[name = tensor("k_37_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_37_end_0 = const()[name = tensor("k_37_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_37_end_mask_0 = const()[name = tensor("k_37_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_37_squeeze_mask_0 = const()[name = tensor("k_37_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_37 = slice_by_index(begin = k_37_begin_0, end = k_37_end_0, end_mask = k_37_end_mask_0, squeeze_mask = k_37_squeeze_mask_0, x = qkv_19)[name = tensor("k_37")]; + tensor v_19_begin_0 = const()[name = tensor("v_19_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_19_end_0 = const()[name = tensor("v_19_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_19_end_mask_0 = const()[name = tensor("v_19_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_19_squeeze_mask_0 = const()[name = tensor("v_19_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_19 = slice_by_index(begin = v_19_begin_0, end = v_19_end_0, end_mask = v_19_end_mask_0, squeeze_mask = v_19_squeeze_mask_0, x = qkv_19)[name = tensor("v_19")]; + tensor freqs_19 = const()[name = tensor("freqs_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172741888)))]; + tensor var_4102 = const()[name = tensor("op_4102"), val = tensor([1, 1, 1, 1])]; + tensor ts_59 = reshape(shape = var_4102, x = position9)[name = tensor("ts_59")]; + tensor var_4106 = const()[name = tensor("op_4106"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_19 = reshape(shape = var_4106, x = q_55)[name = tensor("q_complex_19")]; + tensor var_4110 = const()[name = tensor("op_4110"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_19 = reshape(shape = var_4110, x = k_37)[name = tensor("k_complex_19")]; + tensor var_4114_begin_0 = const()[name = tensor("op_4114_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4114_end_0 = const()[name = tensor("op_4114_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4114_end_mask_0 = const()[name = tensor("op_4114_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4114_squeeze_mask_0 = const()[name = tensor("op_4114_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4114 = slice_by_index(begin = var_4114_begin_0, end = var_4114_end_0, end_mask = var_4114_end_mask_0, squeeze_mask = var_4114_squeeze_mask_0, x = q_complex_19)[name = tensor("op_4114")]; + tensor var_4122_begin_0 = const()[name = tensor("op_4122_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4122_end_0 = const()[name = tensor("op_4122_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4122_end_mask_0 = const()[name = tensor("op_4122_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4122_squeeze_mask_0 = const()[name = tensor("op_4122_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4122 = slice_by_index(begin = var_4122_begin_0, end = var_4122_end_0, end_mask = var_4122_end_mask_0, squeeze_mask = var_4122_squeeze_mask_0, x = q_complex_19)[name = tensor("op_4122")]; + tensor var_4130_begin_0 = const()[name = tensor("op_4130_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4130_end_0 = const()[name = tensor("op_4130_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4130_end_mask_0 = const()[name = tensor("op_4130_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4130_squeeze_mask_0 = const()[name = tensor("op_4130_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4130 = slice_by_index(begin = var_4130_begin_0, end = var_4130_end_0, end_mask = var_4130_end_mask_0, squeeze_mask = var_4130_squeeze_mask_0, x = k_complex_19)[name = tensor("op_4130")]; + tensor var_4138_begin_0 = const()[name = tensor("op_4138_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4138_end_0 = const()[name = tensor("op_4138_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4138_end_mask_0 = const()[name = tensor("op_4138_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4138_squeeze_mask_0 = const()[name = tensor("op_4138_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4138 = slice_by_index(begin = var_4138_begin_0, end = var_4138_end_0, end_mask = var_4138_end_mask_0, squeeze_mask = var_4138_squeeze_mask_0, x = k_complex_19)[name = tensor("op_4138")]; + tensor var_4144 = mul(x = freqs_19, y = ts_59)[name = tensor("op_4144")]; + tensor rotr_19 = cos(x = var_4144)[name = tensor("rotr_19")]; + tensor roti_19 = sin(x = var_4144)[name = tensor("roti_19")]; + tensor var_4148 = mul(x = var_4114, y = rotr_19)[name = tensor("op_4148")]; + tensor var_4149 = mul(x = var_4122, y = roti_19)[name = tensor("op_4149")]; + tensor qor_37 = sub(x = var_4148, y = var_4149)[name = tensor("qor_37")]; + tensor var_4152 = mul(x = var_4114, y = roti_19)[name = tensor("op_4152")]; + tensor var_4153 = mul(x = var_4122, y = rotr_19)[name = tensor("op_4153")]; + tensor qoi_37 = add(x = var_4152, y = var_4153)[name = tensor("qoi_37")]; + tensor var_4156 = mul(x = var_4130, y = rotr_19)[name = tensor("op_4156")]; + tensor var_4157 = mul(x = var_4138, y = roti_19)[name = tensor("op_4157")]; + tensor kor_37 = sub(x = var_4156, y = var_4157)[name = tensor("kor_37")]; + tensor var_4160 = mul(x = var_4130, y = roti_19)[name = tensor("op_4160")]; + tensor var_4161 = mul(x = var_4138, y = rotr_19)[name = tensor("op_4161")]; + tensor koi_37 = add(x = var_4160, y = var_4161)[name = tensor("koi_37")]; + tensor qo_19_axis_0 = const()[name = tensor("qo_19_axis_0"), val = tensor(-1)]; + tensor qo_19 = stack(axis = qo_19_axis_0, values = (qor_37, qoi_37))[name = tensor("qo_19")]; + tensor ko_19_axis_0 = const()[name = tensor("ko_19_axis_0"), val = tensor(-1)]; + tensor ko_19 = stack(axis = ko_19_axis_0, values = (kor_37, koi_37))[name = tensor("ko_19")]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1, 16, 64])]; + tensor q_57 = reshape(shape = var_4190, x = qo_19)[name = tensor("q_57")]; + tensor var_4192 = const()[name = tensor("op_4192"), val = tensor([1, 1, 16, 64])]; + tensor k_39 = reshape(shape = var_4192, x = ko_19)[name = tensor("k_39")]; + tensor _inversed_4214_y_0 = const()[name = tensor("_inversed_4214_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_4214 = mul(x = ts_59, y = _inversed_4214_y_0)[name = tensor("_inversed_4214")]; + tensor var_4215 = floor(x = _inversed_4214)[name = tensor("op_4215")]; + tensor var_4216 = const()[name = tensor("op_4216"), val = tensor(0x1p+9)]; + tensor var_4217 = mul(x = var_4215, y = var_4216)[name = tensor("op_4217")]; + tensor write_indices_float_39 = sub(x = ts_59, y = var_4217)[name = tensor("write_indices_float_39")]; + tensor var_4224_dtype_0 = const()[name = tensor("op_4224_dtype_0"), val = tensor("int32")]; + tensor write_indices_19_reps_0 = const()[name = tensor("write_indices_19_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_4224 = cast(dtype = var_4224_dtype_0, x = write_indices_float_39)[name = tensor("cast_437")]; + tensor write_indices_19 = tile(reps = write_indices_19_reps_0, x = var_4224)[name = tensor("write_indices_19")]; + tensor var_4232_begin_0 = const()[name = tensor("op_4232_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4232_end_0 = const()[name = tensor("op_4232_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_4232_end_mask_0 = const()[name = tensor("op_4232_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4232_squeeze_mask_0 = const()[name = tensor("op_4232_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4232 = slice_by_index(begin = var_4232_begin_0, end = var_4232_end_0, end_mask = var_4232_end_mask_0, squeeze_mask = var_4232_squeeze_mask_0, x = cache9)[name = tensor("op_4232")]; + tensor var_4234_axis_0 = const()[name = tensor("op_4234_axis_0"), val = tensor(1)]; + tensor var_4234_mode_0 = const()[name = tensor("op_4234_mode_0"), val = tensor("update")]; + tensor var_4234_validate_indices_0 = const()[name = tensor("op_4234_validate_indices_0"), val = tensor(false)]; + tensor var_4234 = scatter_along_axis(axis = var_4234_axis_0, data = var_4232, indices = write_indices_19, mode = var_4234_mode_0, updates = k_39, validate_indices = var_4234_validate_indices_0)[name = tensor("op_4234")]; + tensor concat_64 = const()[name = tensor("concat_64"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_65 = const()[name = tensor("concat_65"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_19_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_19_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_64 = const()[name = tensor("shape_64"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_18 = const()[name = tensor("reduce_prod_18"), val = tensor(1048576)]; + tensor range_1d_18_start_0 = const()[name = tensor("range_1d_18_start_0"), val = tensor(0)]; + tensor range_1d_18_step_0 = const()[name = tensor("range_1d_18_step_0"), val = tensor(1)]; + tensor range_1d_18 = range_1d(end = reduce_prod_18, start = range_1d_18_start_0, step = range_1d_18_step_0)[name = tensor("range_1d_18")]; + tensor reshape_90 = reshape(shape = shape_64, x = range_1d_18)[name = tensor("reshape_90")]; + tensor slice_by_index_18 = slice_by_index(begin = concat_64, begin_mask = new_cache_19_internal_tensor_assign_1_begin_mask_0, end = concat_65, end_mask = new_cache_19_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_19_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_19_internal_tensor_assign_1_stride_0, x = reshape_90)[name = tensor("slice_by_index_18")]; + tensor reshape_91_shape_0 = const()[name = tensor("reshape_91_shape_0"), val = tensor([-1])]; + tensor reshape_91 = reshape(shape = reshape_91_shape_0, x = slice_by_index_18)[name = tensor("reshape_91")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([-1])]; + tensor reshape_92 = reshape(shape = reshape_92_shape_0, x = var_4234)[name = tensor("reshape_92")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([-1])]; + tensor reshape_93 = reshape(shape = reshape_93_shape_0, x = cache9)[name = tensor("reshape_93")]; + tensor scatter_18_mode_0 = const()[name = tensor("scatter_18_mode_0"), val = tensor("update")]; + tensor scatter_18_axis_0 = const()[name = tensor("scatter_18_axis_0"), val = tensor(0)]; + tensor scatter_18_validate_indices_0 = const()[name = tensor("scatter_18_validate_indices_0"), val = tensor(false)]; + tensor scatter_18 = scatter(axis = scatter_18_axis_0, data = reshape_93, indices = reshape_91, mode = scatter_18_mode_0, updates = reshape_92, validate_indices = scatter_18_validate_indices_0)[name = tensor("scatter_18")]; + tensor reshape_94 = reshape(shape = shape_64, x = scatter_18)[name = tensor("reshape_94")]; + tensor var_4242_begin_0 = const()[name = tensor("op_4242_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_4242_end_0 = const()[name = tensor("op_4242_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_4242_end_mask_0 = const()[name = tensor("op_4242_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4242_squeeze_mask_0 = const()[name = tensor("op_4242_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4242 = slice_by_index(begin = var_4242_begin_0, end = var_4242_end_0, end_mask = var_4242_end_mask_0, squeeze_mask = var_4242_squeeze_mask_0, x = reshape_94)[name = tensor("op_4242")]; + tensor var_4244_axis_0 = const()[name = tensor("op_4244_axis_0"), val = tensor(1)]; + tensor var_4244_mode_0 = const()[name = tensor("op_4244_mode_0"), val = tensor("update")]; + tensor var_4244_validate_indices_0 = const()[name = tensor("op_4244_validate_indices_0"), val = tensor(false)]; + tensor var_4244 = scatter_along_axis(axis = var_4244_axis_0, data = var_4242, indices = write_indices_19, mode = var_4244_mode_0, updates = v_19, validate_indices = var_4244_validate_indices_0)[name = tensor("op_4244")]; + tensor concat_66 = const()[name = tensor("concat_66"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_67 = const()[name = tensor("concat_67"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_19_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_19_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_65 = const()[name = tensor("shape_65"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_19 = const()[name = tensor("reduce_prod_19"), val = tensor(1048576)]; + tensor range_1d_19_start_0 = const()[name = tensor("range_1d_19_start_0"), val = tensor(0)]; + tensor range_1d_19_step_0 = const()[name = tensor("range_1d_19_step_0"), val = tensor(1)]; + tensor range_1d_19 = range_1d(end = reduce_prod_19, start = range_1d_19_start_0, step = range_1d_19_step_0)[name = tensor("range_1d_19")]; + tensor reshape_95 = reshape(shape = shape_65, x = range_1d_19)[name = tensor("reshape_95")]; + tensor slice_by_index_19 = slice_by_index(begin = concat_66, begin_mask = new_cache_19_internal_tensor_assign_2_begin_mask_0, end = concat_67, end_mask = new_cache_19_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_19_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_19_internal_tensor_assign_2_stride_0, x = reshape_95)[name = tensor("slice_by_index_19")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([-1])]; + tensor reshape_96 = reshape(shape = reshape_96_shape_0, x = slice_by_index_19)[name = tensor("reshape_96")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([-1])]; + tensor reshape_97 = reshape(shape = reshape_97_shape_0, x = var_4244)[name = tensor("reshape_97")]; + tensor reshape_98_shape_0 = const()[name = tensor("reshape_98_shape_0"), val = tensor([-1])]; + tensor reshape_98 = reshape(shape = reshape_98_shape_0, x = reshape_94)[name = tensor("reshape_98")]; + tensor scatter_19_mode_0 = const()[name = tensor("scatter_19_mode_0"), val = tensor("update")]; + tensor scatter_19_axis_0 = const()[name = tensor("scatter_19_axis_0"), val = tensor(0)]; + tensor scatter_19_validate_indices_0 = const()[name = tensor("scatter_19_validate_indices_0"), val = tensor(false)]; + tensor scatter_19 = scatter(axis = scatter_19_axis_0, data = reshape_98, indices = reshape_96, mode = scatter_19_mode_0, updates = reshape_97, validate_indices = scatter_19_validate_indices_0)[name = tensor("scatter_19")]; + tensor new_cache_19_internal_tensor_assign_2 = reshape(shape = shape_65, x = scatter_19)[name = tensor("reshape_99")]; + tensor keys_55_begin_0 = const()[name = tensor("keys_55_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_55_end_0 = const()[name = tensor("keys_55_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_55_end_mask_0 = const()[name = tensor("keys_55_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_55_squeeze_mask_0 = const()[name = tensor("keys_55_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_55 = slice_by_index(begin = keys_55_begin_0, end = keys_55_end_0, end_mask = keys_55_end_mask_0, squeeze_mask = keys_55_squeeze_mask_0, x = new_cache_19_internal_tensor_assign_2)[name = tensor("keys_55")]; + tensor values_55_begin_0 = const()[name = tensor("values_55_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_55_end_0 = const()[name = tensor("values_55_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_55_end_mask_0 = const()[name = tensor("values_55_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_55_squeeze_mask_0 = const()[name = tensor("values_55_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_55 = slice_by_index(begin = values_55_begin_0, end = values_55_end_0, end_mask = values_55_end_mask_0, squeeze_mask = values_55_squeeze_mask_0, x = new_cache_19_internal_tensor_assign_2)[name = tensor("values_55")]; + tensor var_4256 = not_equal(x = keys_55, y = keys_55)[name = tensor("op_4256")]; + tensor keys_57 = select(a = var_491, b = keys_55, cond = var_4256)[name = tensor("keys_57")]; + tensor var_4264 = not_equal(x = values_55, y = values_55)[name = tensor("op_4264")]; + tensor values_57 = select(a = var_491, b = values_55, cond = var_4264)[name = tensor("values_57")]; + tensor var_4288 = const()[name = tensor("op_4288"), val = tensor([0, 2, 1, 3])]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1, 1, 1])]; + tensor var_4302 = reshape(shape = var_4301, x = position9)[name = tensor("op_4302")]; + tensor var_4319 = const()[name = tensor("op_4319"), val = tensor(0x1p+0)]; + tensor valid_len_19 = add(x = var_4302, y = var_4319)[name = tensor("valid_len_19")]; + tensor valid_mask_19 = less(x = k_positions_1_promoted, y = valid_len_19)[name = tensor("valid_mask_19")]; + tensor causal_mask_19 = less_equal(x = k_positions_1_promoted, y = var_4302)[name = tensor("causal_mask_19")]; + tensor attn_mask_37 = logical_and(x = valid_mask_19, y = causal_mask_19)[name = tensor("attn_mask_37")]; + tensor attn_mask_39_axes_0 = const()[name = tensor("attn_mask_39_axes_0"), val = tensor([1])]; + tensor attn_mask_39 = expand_dims(axes = attn_mask_39_axes_0, x = attn_mask_37)[name = tensor("attn_mask_39")]; + tensor var_4331 = const()[name = tensor("op_4331"), val = tensor([0x1.fffe5cp-4])]; + tensor var_4337_transpose_x_0 = const()[name = tensor("op_4337_transpose_x_0"), val = tensor(false)]; + tensor var_4337_transpose_y_0 = const()[name = tensor("op_4337_transpose_y_0"), val = tensor(false)]; + tensor transpose_87_perm_0 = const()[name = tensor("transpose_87_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_88_perm_0 = const()[name = tensor("transpose_88_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = keys_57)[name = tensor("transpose_168")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = q_57)[name = tensor("transpose_169")]; + tensor var_4337 = matmul(transpose_x = var_4337_transpose_x_0, transpose_y = var_4337_transpose_y_0, x = transpose_87, y = transpose_88)[name = tensor("op_4337")]; + tensor attn_weights_55 = mul(x = var_4337, y = var_4331)[name = tensor("attn_weights_55")]; + tensor var_4339 = logical_not(x = attn_mask_39)[name = tensor("op_4339")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_57 = select(a = var_4340, b = attn_weights_55, cond = var_4339)[name = tensor("attn_weights_57")]; + tensor var_4342 = const()[name = tensor("op_4342"), val = tensor(-1)]; + tensor attn_weights_59 = softmax(axis = var_4342, x = attn_weights_57)[name = tensor("attn_weights_59")]; + tensor attn_output_19_transpose_x_0 = const()[name = tensor("attn_output_19_transpose_x_0"), val = tensor(false)]; + tensor attn_output_19_transpose_y_0 = const()[name = tensor("attn_output_19_transpose_y_0"), val = tensor(false)]; + tensor values_59 = transpose(perm = var_4288, x = values_57)[name = tensor("transpose_170")]; + tensor attn_output_19 = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = attn_weights_59, y = values_59)[name = tensor("attn_output_19")]; + tensor var_4350 = const()[name = tensor("op_4350"), val = tensor([0, 2, 1, 3])]; + tensor var_4353 = const()[name = tensor("op_4353"), val = tensor([1, 1, 1024])]; + tensor var_4351 = transpose(perm = var_4350, x = attn_output_19)[name = tensor("transpose_167")]; + tensor input_93 = reshape(shape = var_4353, x = var_4351)[name = tensor("input_93")]; + tensor attn_out_19 = linear(bias = linear_1_bias_0, weight = attn9_out_proj_weight, x = input_93)[name = tensor("linear_37")]; + tensor var_4359 = const()[name = tensor("op_4359"), val = tensor(0x1p+0)]; + tensor var_4360 = add(x = position9, y = var_4359)[name = tensor("op_4360")]; + tensor input_95 = add(x = input_91, y = attn_out_19)[name = tensor("input_95")]; + tensor var_4364 = const()[name = tensor("op_4364"), val = tensor(0x1.4f8b58p-17)]; + tensor input_97_axes_0 = const()[name = tensor("input_97_axes_0"), val = tensor([-1])]; + tensor input_97 = layer_norm(axes = input_97_axes_0, beta = norm9_2_bias, epsilon = var_4364, gamma = norm9_2_weight, x = input_95)[name = tensor("input_97")]; + tensor var_4372 = linear(bias = linear_2_bias_0, weight = linear9_1_weight, x = input_97)[name = tensor("linear_38")]; + tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("EXACT")]; + tensor input_99 = gelu(mode = input_99_mode_0, x = var_4372)[name = tensor("input_99")]; + tensor ffn_out_19 = linear(bias = linear_1_bias_0, weight = linear9_2_weight, x = input_99)[name = tensor("linear_39")]; + tensor input_101 = add(x = input_95, y = ffn_out_19)[name = tensor("input_101")]; + tensor var_4381 = const()[name = tensor("op_4381"), val = tensor(0x1.4f8b58p-17)]; + tensor x_21_axes_0 = const()[name = tensor("x_21_axes_0"), val = tensor([-1])]; + tensor x_21 = layer_norm(axes = x_21_axes_0, beta = norm10_1_bias, epsilon = var_4381, gamma = norm10_1_weight, x = input_101)[name = tensor("x_21")]; + tensor var_4413 = linear(bias = linear_0_bias_0, weight = attn10_in_proj_weight, x = x_21)[name = tensor("linear_40")]; + tensor var_4417 = const()[name = tensor("op_4417"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_21 = reshape(shape = var_4417, x = var_4413)[name = tensor("qkv_21")]; + tensor q_61_begin_0 = const()[name = tensor("q_61_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_61_end_0 = const()[name = tensor("q_61_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_61_end_mask_0 = const()[name = tensor("q_61_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_61_squeeze_mask_0 = const()[name = tensor("q_61_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_61 = slice_by_index(begin = q_61_begin_0, end = q_61_end_0, end_mask = q_61_end_mask_0, squeeze_mask = q_61_squeeze_mask_0, x = qkv_21)[name = tensor("q_61")]; + tensor k_41_begin_0 = const()[name = tensor("k_41_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_41_end_0 = const()[name = tensor("k_41_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_41_end_mask_0 = const()[name = tensor("k_41_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_41_squeeze_mask_0 = const()[name = tensor("k_41_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_41 = slice_by_index(begin = k_41_begin_0, end = k_41_end_0, end_mask = k_41_end_mask_0, squeeze_mask = k_41_squeeze_mask_0, x = qkv_21)[name = tensor("k_41")]; + tensor v_21_begin_0 = const()[name = tensor("v_21_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_21_end_0 = const()[name = tensor("v_21_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_21_end_mask_0 = const()[name = tensor("v_21_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_21_squeeze_mask_0 = const()[name = tensor("v_21_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_21 = slice_by_index(begin = v_21_begin_0, end = v_21_end_0, end_mask = v_21_end_mask_0, squeeze_mask = v_21_squeeze_mask_0, x = qkv_21)[name = tensor("v_21")]; + tensor freqs_21 = const()[name = tensor("freqs_21"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172742080)))]; + tensor var_4521 = const()[name = tensor("op_4521"), val = tensor([1, 1, 1, 1])]; + tensor ts_65 = reshape(shape = var_4521, x = position10)[name = tensor("ts_65")]; + tensor var_4525 = const()[name = tensor("op_4525"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_21 = reshape(shape = var_4525, x = q_61)[name = tensor("q_complex_21")]; + tensor var_4529 = const()[name = tensor("op_4529"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_21 = reshape(shape = var_4529, x = k_41)[name = tensor("k_complex_21")]; + tensor var_4533_begin_0 = const()[name = tensor("op_4533_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4533_end_0 = const()[name = tensor("op_4533_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4533_end_mask_0 = const()[name = tensor("op_4533_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4533_squeeze_mask_0 = const()[name = tensor("op_4533_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4533 = slice_by_index(begin = var_4533_begin_0, end = var_4533_end_0, end_mask = var_4533_end_mask_0, squeeze_mask = var_4533_squeeze_mask_0, x = q_complex_21)[name = tensor("op_4533")]; + tensor var_4541_begin_0 = const()[name = tensor("op_4541_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4541_end_0 = const()[name = tensor("op_4541_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4541_end_mask_0 = const()[name = tensor("op_4541_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4541_squeeze_mask_0 = const()[name = tensor("op_4541_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4541 = slice_by_index(begin = var_4541_begin_0, end = var_4541_end_0, end_mask = var_4541_end_mask_0, squeeze_mask = var_4541_squeeze_mask_0, x = q_complex_21)[name = tensor("op_4541")]; + tensor var_4549_begin_0 = const()[name = tensor("op_4549_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4549_end_0 = const()[name = tensor("op_4549_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4549_end_mask_0 = const()[name = tensor("op_4549_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4549_squeeze_mask_0 = const()[name = tensor("op_4549_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4549 = slice_by_index(begin = var_4549_begin_0, end = var_4549_end_0, end_mask = var_4549_end_mask_0, squeeze_mask = var_4549_squeeze_mask_0, x = k_complex_21)[name = tensor("op_4549")]; + tensor var_4557_begin_0 = const()[name = tensor("op_4557_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4557_end_0 = const()[name = tensor("op_4557_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4557_end_mask_0 = const()[name = tensor("op_4557_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4557_squeeze_mask_0 = const()[name = tensor("op_4557_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4557 = slice_by_index(begin = var_4557_begin_0, end = var_4557_end_0, end_mask = var_4557_end_mask_0, squeeze_mask = var_4557_squeeze_mask_0, x = k_complex_21)[name = tensor("op_4557")]; + tensor var_4563 = mul(x = freqs_21, y = ts_65)[name = tensor("op_4563")]; + tensor rotr_21 = cos(x = var_4563)[name = tensor("rotr_21")]; + tensor roti_21 = sin(x = var_4563)[name = tensor("roti_21")]; + tensor var_4567 = mul(x = var_4533, y = rotr_21)[name = tensor("op_4567")]; + tensor var_4568 = mul(x = var_4541, y = roti_21)[name = tensor("op_4568")]; + tensor qor_41 = sub(x = var_4567, y = var_4568)[name = tensor("qor_41")]; + tensor var_4571 = mul(x = var_4533, y = roti_21)[name = tensor("op_4571")]; + tensor var_4572 = mul(x = var_4541, y = rotr_21)[name = tensor("op_4572")]; + tensor qoi_41 = add(x = var_4571, y = var_4572)[name = tensor("qoi_41")]; + tensor var_4575 = mul(x = var_4549, y = rotr_21)[name = tensor("op_4575")]; + tensor var_4576 = mul(x = var_4557, y = roti_21)[name = tensor("op_4576")]; + tensor kor_41 = sub(x = var_4575, y = var_4576)[name = tensor("kor_41")]; + tensor var_4579 = mul(x = var_4549, y = roti_21)[name = tensor("op_4579")]; + tensor var_4580 = mul(x = var_4557, y = rotr_21)[name = tensor("op_4580")]; + tensor koi_41 = add(x = var_4579, y = var_4580)[name = tensor("koi_41")]; + tensor qo_21_axis_0 = const()[name = tensor("qo_21_axis_0"), val = tensor(-1)]; + tensor qo_21 = stack(axis = qo_21_axis_0, values = (qor_41, qoi_41))[name = tensor("qo_21")]; + tensor ko_21_axis_0 = const()[name = tensor("ko_21_axis_0"), val = tensor(-1)]; + tensor ko_21 = stack(axis = ko_21_axis_0, values = (kor_41, koi_41))[name = tensor("ko_21")]; + tensor var_4609 = const()[name = tensor("op_4609"), val = tensor([1, 1, 16, 64])]; + tensor q_63 = reshape(shape = var_4609, x = qo_21)[name = tensor("q_63")]; + tensor var_4611 = const()[name = tensor("op_4611"), val = tensor([1, 1, 16, 64])]; + tensor k_43 = reshape(shape = var_4611, x = ko_21)[name = tensor("k_43")]; + tensor _inversed_4633_y_0 = const()[name = tensor("_inversed_4633_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_4633 = mul(x = ts_65, y = _inversed_4633_y_0)[name = tensor("_inversed_4633")]; + tensor var_4634 = floor(x = _inversed_4633)[name = tensor("op_4634")]; + tensor var_4635 = const()[name = tensor("op_4635"), val = tensor(0x1p+9)]; + tensor var_4636 = mul(x = var_4634, y = var_4635)[name = tensor("op_4636")]; + tensor write_indices_float_43 = sub(x = ts_65, y = var_4636)[name = tensor("write_indices_float_43")]; + tensor var_4643_dtype_0 = const()[name = tensor("op_4643_dtype_0"), val = tensor("int32")]; + tensor write_indices_21_reps_0 = const()[name = tensor("write_indices_21_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_4643 = cast(dtype = var_4643_dtype_0, x = write_indices_float_43)[name = tensor("cast_436")]; + tensor write_indices_21 = tile(reps = write_indices_21_reps_0, x = var_4643)[name = tensor("write_indices_21")]; + tensor var_4651_begin_0 = const()[name = tensor("op_4651_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4651_end_0 = const()[name = tensor("op_4651_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_4651_end_mask_0 = const()[name = tensor("op_4651_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4651_squeeze_mask_0 = const()[name = tensor("op_4651_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4651 = slice_by_index(begin = var_4651_begin_0, end = var_4651_end_0, end_mask = var_4651_end_mask_0, squeeze_mask = var_4651_squeeze_mask_0, x = cache10)[name = tensor("op_4651")]; + tensor var_4653_axis_0 = const()[name = tensor("op_4653_axis_0"), val = tensor(1)]; + tensor var_4653_mode_0 = const()[name = tensor("op_4653_mode_0"), val = tensor("update")]; + tensor var_4653_validate_indices_0 = const()[name = tensor("op_4653_validate_indices_0"), val = tensor(false)]; + tensor var_4653 = scatter_along_axis(axis = var_4653_axis_0, data = var_4651, indices = write_indices_21, mode = var_4653_mode_0, updates = k_43, validate_indices = var_4653_validate_indices_0)[name = tensor("op_4653")]; + tensor concat_71 = const()[name = tensor("concat_71"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_72 = const()[name = tensor("concat_72"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_21_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_21_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_66 = const()[name = tensor("shape_66"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_20 = const()[name = tensor("reduce_prod_20"), val = tensor(1048576)]; + tensor range_1d_20_start_0 = const()[name = tensor("range_1d_20_start_0"), val = tensor(0)]; + tensor range_1d_20_step_0 = const()[name = tensor("range_1d_20_step_0"), val = tensor(1)]; + tensor range_1d_20 = range_1d(end = reduce_prod_20, start = range_1d_20_start_0, step = range_1d_20_step_0)[name = tensor("range_1d_20")]; + tensor reshape_100 = reshape(shape = shape_66, x = range_1d_20)[name = tensor("reshape_100")]; + tensor slice_by_index_20 = slice_by_index(begin = concat_71, begin_mask = new_cache_21_internal_tensor_assign_1_begin_mask_0, end = concat_72, end_mask = new_cache_21_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_21_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_21_internal_tensor_assign_1_stride_0, x = reshape_100)[name = tensor("slice_by_index_20")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([-1])]; + tensor reshape_101 = reshape(shape = reshape_101_shape_0, x = slice_by_index_20)[name = tensor("reshape_101")]; + tensor reshape_102_shape_0 = const()[name = tensor("reshape_102_shape_0"), val = tensor([-1])]; + tensor reshape_102 = reshape(shape = reshape_102_shape_0, x = var_4653)[name = tensor("reshape_102")]; + tensor reshape_103_shape_0 = const()[name = tensor("reshape_103_shape_0"), val = tensor([-1])]; + tensor reshape_103 = reshape(shape = reshape_103_shape_0, x = cache10)[name = tensor("reshape_103")]; + tensor scatter_20_mode_0 = const()[name = tensor("scatter_20_mode_0"), val = tensor("update")]; + tensor scatter_20_axis_0 = const()[name = tensor("scatter_20_axis_0"), val = tensor(0)]; + tensor scatter_20_validate_indices_0 = const()[name = tensor("scatter_20_validate_indices_0"), val = tensor(false)]; + tensor scatter_20 = scatter(axis = scatter_20_axis_0, data = reshape_103, indices = reshape_101, mode = scatter_20_mode_0, updates = reshape_102, validate_indices = scatter_20_validate_indices_0)[name = tensor("scatter_20")]; + tensor reshape_104 = reshape(shape = shape_66, x = scatter_20)[name = tensor("reshape_104")]; + tensor var_4661_begin_0 = const()[name = tensor("op_4661_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_4661_end_0 = const()[name = tensor("op_4661_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_4661_end_mask_0 = const()[name = tensor("op_4661_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4661_squeeze_mask_0 = const()[name = tensor("op_4661_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4661 = slice_by_index(begin = var_4661_begin_0, end = var_4661_end_0, end_mask = var_4661_end_mask_0, squeeze_mask = var_4661_squeeze_mask_0, x = reshape_104)[name = tensor("op_4661")]; + tensor var_4663_axis_0 = const()[name = tensor("op_4663_axis_0"), val = tensor(1)]; + tensor var_4663_mode_0 = const()[name = tensor("op_4663_mode_0"), val = tensor("update")]; + tensor var_4663_validate_indices_0 = const()[name = tensor("op_4663_validate_indices_0"), val = tensor(false)]; + tensor var_4663 = scatter_along_axis(axis = var_4663_axis_0, data = var_4661, indices = write_indices_21, mode = var_4663_mode_0, updates = v_21, validate_indices = var_4663_validate_indices_0)[name = tensor("op_4663")]; + tensor concat_73 = const()[name = tensor("concat_73"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_74 = const()[name = tensor("concat_74"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_21_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_21_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_67 = const()[name = tensor("shape_67"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_21 = const()[name = tensor("reduce_prod_21"), val = tensor(1048576)]; + tensor range_1d_21_start_0 = const()[name = tensor("range_1d_21_start_0"), val = tensor(0)]; + tensor range_1d_21_step_0 = const()[name = tensor("range_1d_21_step_0"), val = tensor(1)]; + tensor range_1d_21 = range_1d(end = reduce_prod_21, start = range_1d_21_start_0, step = range_1d_21_step_0)[name = tensor("range_1d_21")]; + tensor reshape_105 = reshape(shape = shape_67, x = range_1d_21)[name = tensor("reshape_105")]; + tensor slice_by_index_21 = slice_by_index(begin = concat_73, begin_mask = new_cache_21_internal_tensor_assign_2_begin_mask_0, end = concat_74, end_mask = new_cache_21_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_21_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_21_internal_tensor_assign_2_stride_0, x = reshape_105)[name = tensor("slice_by_index_21")]; + tensor reshape_106_shape_0 = const()[name = tensor("reshape_106_shape_0"), val = tensor([-1])]; + tensor reshape_106 = reshape(shape = reshape_106_shape_0, x = slice_by_index_21)[name = tensor("reshape_106")]; + tensor reshape_107_shape_0 = const()[name = tensor("reshape_107_shape_0"), val = tensor([-1])]; + tensor reshape_107 = reshape(shape = reshape_107_shape_0, x = var_4663)[name = tensor("reshape_107")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([-1])]; + tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = reshape_104)[name = tensor("reshape_108")]; + tensor scatter_21_mode_0 = const()[name = tensor("scatter_21_mode_0"), val = tensor("update")]; + tensor scatter_21_axis_0 = const()[name = tensor("scatter_21_axis_0"), val = tensor(0)]; + tensor scatter_21_validate_indices_0 = const()[name = tensor("scatter_21_validate_indices_0"), val = tensor(false)]; + tensor scatter_21 = scatter(axis = scatter_21_axis_0, data = reshape_108, indices = reshape_106, mode = scatter_21_mode_0, updates = reshape_107, validate_indices = scatter_21_validate_indices_0)[name = tensor("scatter_21")]; + tensor new_cache_21_internal_tensor_assign_2 = reshape(shape = shape_67, x = scatter_21)[name = tensor("reshape_109")]; + tensor keys_61_begin_0 = const()[name = tensor("keys_61_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_61_end_0 = const()[name = tensor("keys_61_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_61_end_mask_0 = const()[name = tensor("keys_61_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_61_squeeze_mask_0 = const()[name = tensor("keys_61_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_61 = slice_by_index(begin = keys_61_begin_0, end = keys_61_end_0, end_mask = keys_61_end_mask_0, squeeze_mask = keys_61_squeeze_mask_0, x = new_cache_21_internal_tensor_assign_2)[name = tensor("keys_61")]; + tensor values_61_begin_0 = const()[name = tensor("values_61_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_61_end_0 = const()[name = tensor("values_61_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_61_end_mask_0 = const()[name = tensor("values_61_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_61_squeeze_mask_0 = const()[name = tensor("values_61_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_61 = slice_by_index(begin = values_61_begin_0, end = values_61_end_0, end_mask = values_61_end_mask_0, squeeze_mask = values_61_squeeze_mask_0, x = new_cache_21_internal_tensor_assign_2)[name = tensor("values_61")]; + tensor var_4675 = not_equal(x = keys_61, y = keys_61)[name = tensor("op_4675")]; + tensor keys_63 = select(a = var_491, b = keys_61, cond = var_4675)[name = tensor("keys_63")]; + tensor var_4683 = not_equal(x = values_61, y = values_61)[name = tensor("op_4683")]; + tensor values_63 = select(a = var_491, b = values_61, cond = var_4683)[name = tensor("values_63")]; + tensor var_4707 = const()[name = tensor("op_4707"), val = tensor([0, 2, 1, 3])]; + tensor var_4720 = const()[name = tensor("op_4720"), val = tensor([1, 1, 1])]; + tensor var_4721 = reshape(shape = var_4720, x = position10)[name = tensor("op_4721")]; + tensor var_4738 = const()[name = tensor("op_4738"), val = tensor(0x1p+0)]; + tensor valid_len_21 = add(x = var_4721, y = var_4738)[name = tensor("valid_len_21")]; + tensor valid_mask_21 = less(x = k_positions_1_promoted, y = valid_len_21)[name = tensor("valid_mask_21")]; + tensor causal_mask_21 = less_equal(x = k_positions_1_promoted, y = var_4721)[name = tensor("causal_mask_21")]; + tensor attn_mask_41 = logical_and(x = valid_mask_21, y = causal_mask_21)[name = tensor("attn_mask_41")]; + tensor attn_mask_43_axes_0 = const()[name = tensor("attn_mask_43_axes_0"), val = tensor([1])]; + tensor attn_mask_43 = expand_dims(axes = attn_mask_43_axes_0, x = attn_mask_41)[name = tensor("attn_mask_43")]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([0x1.fffe5cp-4])]; + tensor var_4756_transpose_x_0 = const()[name = tensor("op_4756_transpose_x_0"), val = tensor(false)]; + tensor var_4756_transpose_y_0 = const()[name = tensor("op_4756_transpose_y_0"), val = tensor(false)]; + tensor transpose_89_perm_0 = const()[name = tensor("transpose_89_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_90_perm_0 = const()[name = tensor("transpose_90_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = keys_63)[name = tensor("transpose_164")]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = q_63)[name = tensor("transpose_165")]; + tensor var_4756 = matmul(transpose_x = var_4756_transpose_x_0, transpose_y = var_4756_transpose_y_0, x = transpose_89, y = transpose_90)[name = tensor("op_4756")]; + tensor attn_weights_61 = mul(x = var_4756, y = var_4750)[name = tensor("attn_weights_61")]; + tensor var_4758 = logical_not(x = attn_mask_43)[name = tensor("op_4758")]; + tensor var_4759 = const()[name = tensor("op_4759"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_63 = select(a = var_4759, b = attn_weights_61, cond = var_4758)[name = tensor("attn_weights_63")]; + tensor var_4761 = const()[name = tensor("op_4761"), val = tensor(-1)]; + tensor attn_weights_65 = softmax(axis = var_4761, x = attn_weights_63)[name = tensor("attn_weights_65")]; + tensor attn_output_21_transpose_x_0 = const()[name = tensor("attn_output_21_transpose_x_0"), val = tensor(false)]; + tensor attn_output_21_transpose_y_0 = const()[name = tensor("attn_output_21_transpose_y_0"), val = tensor(false)]; + tensor values_65 = transpose(perm = var_4707, x = values_63)[name = tensor("transpose_166")]; + tensor attn_output_21 = matmul(transpose_x = attn_output_21_transpose_x_0, transpose_y = attn_output_21_transpose_y_0, x = attn_weights_65, y = values_65)[name = tensor("attn_output_21")]; + tensor var_4769 = const()[name = tensor("op_4769"), val = tensor([0, 2, 1, 3])]; + tensor var_4772 = const()[name = tensor("op_4772"), val = tensor([1, 1, 1024])]; + tensor var_4770 = transpose(perm = var_4769, x = attn_output_21)[name = tensor("transpose_163")]; + tensor input_103 = reshape(shape = var_4772, x = var_4770)[name = tensor("input_103")]; + tensor attn_out_21 = linear(bias = linear_1_bias_0, weight = attn10_out_proj_weight, x = input_103)[name = tensor("linear_41")]; + tensor var_4778 = const()[name = tensor("op_4778"), val = tensor(0x1p+0)]; + tensor var_4779 = add(x = position10, y = var_4778)[name = tensor("op_4779")]; + tensor input_105 = add(x = input_101, y = attn_out_21)[name = tensor("input_105")]; + tensor var_4783 = const()[name = tensor("op_4783"), val = tensor(0x1.4f8b58p-17)]; + tensor input_107_axes_0 = const()[name = tensor("input_107_axes_0"), val = tensor([-1])]; + tensor input_107 = layer_norm(axes = input_107_axes_0, beta = norm10_2_bias, epsilon = var_4783, gamma = norm10_2_weight, x = input_105)[name = tensor("input_107")]; + tensor var_4791 = linear(bias = linear_2_bias_0, weight = linear10_1_weight, x = input_107)[name = tensor("linear_42")]; + tensor input_109_mode_0 = const()[name = tensor("input_109_mode_0"), val = tensor("EXACT")]; + tensor input_109 = gelu(mode = input_109_mode_0, x = var_4791)[name = tensor("input_109")]; + tensor ffn_out_21 = linear(bias = linear_1_bias_0, weight = linear10_2_weight, x = input_109)[name = tensor("linear_43")]; + tensor input_111 = add(x = input_105, y = ffn_out_21)[name = tensor("input_111")]; + tensor var_4800 = const()[name = tensor("op_4800"), val = tensor(0x1.4f8b58p-17)]; + tensor x_23_axes_0 = const()[name = tensor("x_23_axes_0"), val = tensor([-1])]; + tensor x_23 = layer_norm(axes = x_23_axes_0, beta = norm11_1_bias, epsilon = var_4800, gamma = norm11_1_weight, x = input_111)[name = tensor("x_23")]; + tensor var_4832 = linear(bias = linear_0_bias_0, weight = attn11_in_proj_weight, x = x_23)[name = tensor("linear_44")]; + tensor var_4836 = const()[name = tensor("op_4836"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_23 = reshape(shape = var_4836, x = var_4832)[name = tensor("qkv_23")]; + tensor q_67_begin_0 = const()[name = tensor("q_67_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_67_end_0 = const()[name = tensor("q_67_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_67_end_mask_0 = const()[name = tensor("q_67_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_67_squeeze_mask_0 = const()[name = tensor("q_67_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_67 = slice_by_index(begin = q_67_begin_0, end = q_67_end_0, end_mask = q_67_end_mask_0, squeeze_mask = q_67_squeeze_mask_0, x = qkv_23)[name = tensor("q_67")]; + tensor k_45_begin_0 = const()[name = tensor("k_45_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_45_end_0 = const()[name = tensor("k_45_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_45_end_mask_0 = const()[name = tensor("k_45_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_45_squeeze_mask_0 = const()[name = tensor("k_45_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_45 = slice_by_index(begin = k_45_begin_0, end = k_45_end_0, end_mask = k_45_end_mask_0, squeeze_mask = k_45_squeeze_mask_0, x = qkv_23)[name = tensor("k_45")]; + tensor v_23_begin_0 = const()[name = tensor("v_23_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_23_end_0 = const()[name = tensor("v_23_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_23_end_mask_0 = const()[name = tensor("v_23_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_23_squeeze_mask_0 = const()[name = tensor("v_23_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_23 = slice_by_index(begin = v_23_begin_0, end = v_23_end_0, end_mask = v_23_end_mask_0, squeeze_mask = v_23_squeeze_mask_0, x = qkv_23)[name = tensor("v_23")]; + tensor freqs_23 = const()[name = tensor("freqs_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172742272)))]; + tensor var_4940 = const()[name = tensor("op_4940"), val = tensor([1, 1, 1, 1])]; + tensor ts_71 = reshape(shape = var_4940, x = position11)[name = tensor("ts_71")]; + tensor var_4944 = const()[name = tensor("op_4944"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_23 = reshape(shape = var_4944, x = q_67)[name = tensor("q_complex_23")]; + tensor var_4948 = const()[name = tensor("op_4948"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_23 = reshape(shape = var_4948, x = k_45)[name = tensor("k_complex_23")]; + tensor var_4952_begin_0 = const()[name = tensor("op_4952_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4952_end_0 = const()[name = tensor("op_4952_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4952_end_mask_0 = const()[name = tensor("op_4952_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4952_squeeze_mask_0 = const()[name = tensor("op_4952_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4952 = slice_by_index(begin = var_4952_begin_0, end = var_4952_end_0, end_mask = var_4952_end_mask_0, squeeze_mask = var_4952_squeeze_mask_0, x = q_complex_23)[name = tensor("op_4952")]; + tensor var_4960_begin_0 = const()[name = tensor("op_4960_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4960_end_0 = const()[name = tensor("op_4960_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4960_end_mask_0 = const()[name = tensor("op_4960_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4960_squeeze_mask_0 = const()[name = tensor("op_4960_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4960 = slice_by_index(begin = var_4960_begin_0, end = var_4960_end_0, end_mask = var_4960_end_mask_0, squeeze_mask = var_4960_squeeze_mask_0, x = q_complex_23)[name = tensor("op_4960")]; + tensor var_4968_begin_0 = const()[name = tensor("op_4968_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4968_end_0 = const()[name = tensor("op_4968_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4968_end_mask_0 = const()[name = tensor("op_4968_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4968_squeeze_mask_0 = const()[name = tensor("op_4968_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4968 = slice_by_index(begin = var_4968_begin_0, end = var_4968_end_0, end_mask = var_4968_end_mask_0, squeeze_mask = var_4968_squeeze_mask_0, x = k_complex_23)[name = tensor("op_4968")]; + tensor var_4976_begin_0 = const()[name = tensor("op_4976_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4976_end_0 = const()[name = tensor("op_4976_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4976_end_mask_0 = const()[name = tensor("op_4976_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4976_squeeze_mask_0 = const()[name = tensor("op_4976_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4976 = slice_by_index(begin = var_4976_begin_0, end = var_4976_end_0, end_mask = var_4976_end_mask_0, squeeze_mask = var_4976_squeeze_mask_0, x = k_complex_23)[name = tensor("op_4976")]; + tensor var_4982 = mul(x = freqs_23, y = ts_71)[name = tensor("op_4982")]; + tensor rotr_23 = cos(x = var_4982)[name = tensor("rotr_23")]; + tensor roti_23 = sin(x = var_4982)[name = tensor("roti_23")]; + tensor var_4986 = mul(x = var_4952, y = rotr_23)[name = tensor("op_4986")]; + tensor var_4987 = mul(x = var_4960, y = roti_23)[name = tensor("op_4987")]; + tensor qor_45 = sub(x = var_4986, y = var_4987)[name = tensor("qor_45")]; + tensor var_4990 = mul(x = var_4952, y = roti_23)[name = tensor("op_4990")]; + tensor var_4991 = mul(x = var_4960, y = rotr_23)[name = tensor("op_4991")]; + tensor qoi_45 = add(x = var_4990, y = var_4991)[name = tensor("qoi_45")]; + tensor var_4994 = mul(x = var_4968, y = rotr_23)[name = tensor("op_4994")]; + tensor var_4995 = mul(x = var_4976, y = roti_23)[name = tensor("op_4995")]; + tensor kor_45 = sub(x = var_4994, y = var_4995)[name = tensor("kor_45")]; + tensor var_4998 = mul(x = var_4968, y = roti_23)[name = tensor("op_4998")]; + tensor var_4999 = mul(x = var_4976, y = rotr_23)[name = tensor("op_4999")]; + tensor koi_45 = add(x = var_4998, y = var_4999)[name = tensor("koi_45")]; + tensor qo_23_axis_0 = const()[name = tensor("qo_23_axis_0"), val = tensor(-1)]; + tensor qo_23 = stack(axis = qo_23_axis_0, values = (qor_45, qoi_45))[name = tensor("qo_23")]; + tensor ko_23_axis_0 = const()[name = tensor("ko_23_axis_0"), val = tensor(-1)]; + tensor ko_23 = stack(axis = ko_23_axis_0, values = (kor_45, koi_45))[name = tensor("ko_23")]; + tensor var_5028 = const()[name = tensor("op_5028"), val = tensor([1, 1, 16, 64])]; + tensor q_69 = reshape(shape = var_5028, x = qo_23)[name = tensor("q_69")]; + tensor var_5030 = const()[name = tensor("op_5030"), val = tensor([1, 1, 16, 64])]; + tensor k_47 = reshape(shape = var_5030, x = ko_23)[name = tensor("k_47")]; + tensor _inversed_5052_y_0 = const()[name = tensor("_inversed_5052_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_5052 = mul(x = ts_71, y = _inversed_5052_y_0)[name = tensor("_inversed_5052")]; + tensor var_5053 = floor(x = _inversed_5052)[name = tensor("op_5053")]; + tensor var_5054 = const()[name = tensor("op_5054"), val = tensor(0x1p+9)]; + tensor var_5055 = mul(x = var_5053, y = var_5054)[name = tensor("op_5055")]; + tensor write_indices_float_47 = sub(x = ts_71, y = var_5055)[name = tensor("write_indices_float_47")]; + tensor var_5062_dtype_0 = const()[name = tensor("op_5062_dtype_0"), val = tensor("int32")]; + tensor write_indices_23_reps_0 = const()[name = tensor("write_indices_23_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_5062 = cast(dtype = var_5062_dtype_0, x = write_indices_float_47)[name = tensor("cast_435")]; + tensor write_indices_23 = tile(reps = write_indices_23_reps_0, x = var_5062)[name = tensor("write_indices_23")]; + tensor var_5070_begin_0 = const()[name = tensor("op_5070_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5070_end_0 = const()[name = tensor("op_5070_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_5070_end_mask_0 = const()[name = tensor("op_5070_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5070_squeeze_mask_0 = const()[name = tensor("op_5070_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5070 = slice_by_index(begin = var_5070_begin_0, end = var_5070_end_0, end_mask = var_5070_end_mask_0, squeeze_mask = var_5070_squeeze_mask_0, x = cache11)[name = tensor("op_5070")]; + tensor var_5072_axis_0 = const()[name = tensor("op_5072_axis_0"), val = tensor(1)]; + tensor var_5072_mode_0 = const()[name = tensor("op_5072_mode_0"), val = tensor("update")]; + tensor var_5072_validate_indices_0 = const()[name = tensor("op_5072_validate_indices_0"), val = tensor(false)]; + tensor var_5072 = scatter_along_axis(axis = var_5072_axis_0, data = var_5070, indices = write_indices_23, mode = var_5072_mode_0, updates = k_47, validate_indices = var_5072_validate_indices_0)[name = tensor("op_5072")]; + tensor concat_78 = const()[name = tensor("concat_78"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_79 = const()[name = tensor("concat_79"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_23_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_23_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_68 = const()[name = tensor("shape_68"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_22 = const()[name = tensor("reduce_prod_22"), val = tensor(1048576)]; + tensor range_1d_22_start_0 = const()[name = tensor("range_1d_22_start_0"), val = tensor(0)]; + tensor range_1d_22_step_0 = const()[name = tensor("range_1d_22_step_0"), val = tensor(1)]; + tensor range_1d_22 = range_1d(end = reduce_prod_22, start = range_1d_22_start_0, step = range_1d_22_step_0)[name = tensor("range_1d_22")]; + tensor reshape_110 = reshape(shape = shape_68, x = range_1d_22)[name = tensor("reshape_110")]; + tensor slice_by_index_22 = slice_by_index(begin = concat_78, begin_mask = new_cache_23_internal_tensor_assign_1_begin_mask_0, end = concat_79, end_mask = new_cache_23_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_23_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_23_internal_tensor_assign_1_stride_0, x = reshape_110)[name = tensor("slice_by_index_22")]; + tensor reshape_111_shape_0 = const()[name = tensor("reshape_111_shape_0"), val = tensor([-1])]; + tensor reshape_111 = reshape(shape = reshape_111_shape_0, x = slice_by_index_22)[name = tensor("reshape_111")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([-1])]; + tensor reshape_112 = reshape(shape = reshape_112_shape_0, x = var_5072)[name = tensor("reshape_112")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([-1])]; + tensor reshape_113 = reshape(shape = reshape_113_shape_0, x = cache11)[name = tensor("reshape_113")]; + tensor scatter_22_mode_0 = const()[name = tensor("scatter_22_mode_0"), val = tensor("update")]; + tensor scatter_22_axis_0 = const()[name = tensor("scatter_22_axis_0"), val = tensor(0)]; + tensor scatter_22_validate_indices_0 = const()[name = tensor("scatter_22_validate_indices_0"), val = tensor(false)]; + tensor scatter_22 = scatter(axis = scatter_22_axis_0, data = reshape_113, indices = reshape_111, mode = scatter_22_mode_0, updates = reshape_112, validate_indices = scatter_22_validate_indices_0)[name = tensor("scatter_22")]; + tensor reshape_114 = reshape(shape = shape_68, x = scatter_22)[name = tensor("reshape_114")]; + tensor var_5080_begin_0 = const()[name = tensor("op_5080_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_5080_end_0 = const()[name = tensor("op_5080_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_5080_end_mask_0 = const()[name = tensor("op_5080_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5080_squeeze_mask_0 = const()[name = tensor("op_5080_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5080 = slice_by_index(begin = var_5080_begin_0, end = var_5080_end_0, end_mask = var_5080_end_mask_0, squeeze_mask = var_5080_squeeze_mask_0, x = reshape_114)[name = tensor("op_5080")]; + tensor var_5082_axis_0 = const()[name = tensor("op_5082_axis_0"), val = tensor(1)]; + tensor var_5082_mode_0 = const()[name = tensor("op_5082_mode_0"), val = tensor("update")]; + tensor var_5082_validate_indices_0 = const()[name = tensor("op_5082_validate_indices_0"), val = tensor(false)]; + tensor var_5082 = scatter_along_axis(axis = var_5082_axis_0, data = var_5080, indices = write_indices_23, mode = var_5082_mode_0, updates = v_23, validate_indices = var_5082_validate_indices_0)[name = tensor("op_5082")]; + tensor concat_80 = const()[name = tensor("concat_80"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_81 = const()[name = tensor("concat_81"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_23_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_23_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_69 = const()[name = tensor("shape_69"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_23 = const()[name = tensor("reduce_prod_23"), val = tensor(1048576)]; + tensor range_1d_23_start_0 = const()[name = tensor("range_1d_23_start_0"), val = tensor(0)]; + tensor range_1d_23_step_0 = const()[name = tensor("range_1d_23_step_0"), val = tensor(1)]; + tensor range_1d_23 = range_1d(end = reduce_prod_23, start = range_1d_23_start_0, step = range_1d_23_step_0)[name = tensor("range_1d_23")]; + tensor reshape_115 = reshape(shape = shape_69, x = range_1d_23)[name = tensor("reshape_115")]; + tensor slice_by_index_23 = slice_by_index(begin = concat_80, begin_mask = new_cache_23_internal_tensor_assign_2_begin_mask_0, end = concat_81, end_mask = new_cache_23_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_23_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_23_internal_tensor_assign_2_stride_0, x = reshape_115)[name = tensor("slice_by_index_23")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([-1])]; + tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = slice_by_index_23)[name = tensor("reshape_116")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([-1])]; + tensor reshape_117 = reshape(shape = reshape_117_shape_0, x = var_5082)[name = tensor("reshape_117")]; + tensor reshape_118_shape_0 = const()[name = tensor("reshape_118_shape_0"), val = tensor([-1])]; + tensor reshape_118 = reshape(shape = reshape_118_shape_0, x = reshape_114)[name = tensor("reshape_118")]; + tensor scatter_23_mode_0 = const()[name = tensor("scatter_23_mode_0"), val = tensor("update")]; + tensor scatter_23_axis_0 = const()[name = tensor("scatter_23_axis_0"), val = tensor(0)]; + tensor scatter_23_validate_indices_0 = const()[name = tensor("scatter_23_validate_indices_0"), val = tensor(false)]; + tensor scatter_23 = scatter(axis = scatter_23_axis_0, data = reshape_118, indices = reshape_116, mode = scatter_23_mode_0, updates = reshape_117, validate_indices = scatter_23_validate_indices_0)[name = tensor("scatter_23")]; + tensor new_cache_23_internal_tensor_assign_2 = reshape(shape = shape_69, x = scatter_23)[name = tensor("reshape_119")]; + tensor keys_67_begin_0 = const()[name = tensor("keys_67_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_67_end_0 = const()[name = tensor("keys_67_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_67_end_mask_0 = const()[name = tensor("keys_67_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_67_squeeze_mask_0 = const()[name = tensor("keys_67_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_67 = slice_by_index(begin = keys_67_begin_0, end = keys_67_end_0, end_mask = keys_67_end_mask_0, squeeze_mask = keys_67_squeeze_mask_0, x = new_cache_23_internal_tensor_assign_2)[name = tensor("keys_67")]; + tensor values_67_begin_0 = const()[name = tensor("values_67_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_67_end_0 = const()[name = tensor("values_67_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_67_end_mask_0 = const()[name = tensor("values_67_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_67_squeeze_mask_0 = const()[name = tensor("values_67_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_67 = slice_by_index(begin = values_67_begin_0, end = values_67_end_0, end_mask = values_67_end_mask_0, squeeze_mask = values_67_squeeze_mask_0, x = new_cache_23_internal_tensor_assign_2)[name = tensor("values_67")]; + tensor var_5094 = not_equal(x = keys_67, y = keys_67)[name = tensor("op_5094")]; + tensor keys_69 = select(a = var_491, b = keys_67, cond = var_5094)[name = tensor("keys_69")]; + tensor var_5102 = not_equal(x = values_67, y = values_67)[name = tensor("op_5102")]; + tensor values_69 = select(a = var_491, b = values_67, cond = var_5102)[name = tensor("values_69")]; + tensor var_5126 = const()[name = tensor("op_5126"), val = tensor([0, 2, 1, 3])]; + tensor var_5139 = const()[name = tensor("op_5139"), val = tensor([1, 1, 1])]; + tensor var_5140 = reshape(shape = var_5139, x = position11)[name = tensor("op_5140")]; + tensor var_5157 = const()[name = tensor("op_5157"), val = tensor(0x1p+0)]; + tensor valid_len_23 = add(x = var_5140, y = var_5157)[name = tensor("valid_len_23")]; + tensor valid_mask_23 = less(x = k_positions_1_promoted, y = valid_len_23)[name = tensor("valid_mask_23")]; + tensor causal_mask_23 = less_equal(x = k_positions_1_promoted, y = var_5140)[name = tensor("causal_mask_23")]; + tensor attn_mask_45 = logical_and(x = valid_mask_23, y = causal_mask_23)[name = tensor("attn_mask_45")]; + tensor attn_mask_47_axes_0 = const()[name = tensor("attn_mask_47_axes_0"), val = tensor([1])]; + tensor attn_mask_47 = expand_dims(axes = attn_mask_47_axes_0, x = attn_mask_45)[name = tensor("attn_mask_47")]; + tensor var_5169 = const()[name = tensor("op_5169"), val = tensor([0x1.fffe5cp-4])]; + tensor var_5175_transpose_x_0 = const()[name = tensor("op_5175_transpose_x_0"), val = tensor(false)]; + tensor var_5175_transpose_y_0 = const()[name = tensor("op_5175_transpose_y_0"), val = tensor(false)]; + tensor transpose_91_perm_0 = const()[name = tensor("transpose_91_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_92_perm_0 = const()[name = tensor("transpose_92_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = keys_69)[name = tensor("transpose_160")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = q_69)[name = tensor("transpose_161")]; + tensor var_5175 = matmul(transpose_x = var_5175_transpose_x_0, transpose_y = var_5175_transpose_y_0, x = transpose_91, y = transpose_92)[name = tensor("op_5175")]; + tensor attn_weights_67 = mul(x = var_5175, y = var_5169)[name = tensor("attn_weights_67")]; + tensor var_5177 = logical_not(x = attn_mask_47)[name = tensor("op_5177")]; + tensor var_5178 = const()[name = tensor("op_5178"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_69 = select(a = var_5178, b = attn_weights_67, cond = var_5177)[name = tensor("attn_weights_69")]; + tensor var_5180 = const()[name = tensor("op_5180"), val = tensor(-1)]; + tensor attn_weights_71 = softmax(axis = var_5180, x = attn_weights_69)[name = tensor("attn_weights_71")]; + tensor attn_output_23_transpose_x_0 = const()[name = tensor("attn_output_23_transpose_x_0"), val = tensor(false)]; + tensor attn_output_23_transpose_y_0 = const()[name = tensor("attn_output_23_transpose_y_0"), val = tensor(false)]; + tensor values_71 = transpose(perm = var_5126, x = values_69)[name = tensor("transpose_162")]; + tensor attn_output_23 = matmul(transpose_x = attn_output_23_transpose_x_0, transpose_y = attn_output_23_transpose_y_0, x = attn_weights_71, y = values_71)[name = tensor("attn_output_23")]; + tensor var_5188 = const()[name = tensor("op_5188"), val = tensor([0, 2, 1, 3])]; + tensor var_5191 = const()[name = tensor("op_5191"), val = tensor([1, 1, 1024])]; + tensor var_5189 = transpose(perm = var_5188, x = attn_output_23)[name = tensor("transpose_159")]; + tensor input_113 = reshape(shape = var_5191, x = var_5189)[name = tensor("input_113")]; + tensor attn_out_23 = linear(bias = linear_1_bias_0, weight = attn11_out_proj_weight, x = input_113)[name = tensor("linear_45")]; + tensor var_5197 = const()[name = tensor("op_5197"), val = tensor(0x1p+0)]; + tensor var_5198 = add(x = position11, y = var_5197)[name = tensor("op_5198")]; + tensor input_115 = add(x = input_111, y = attn_out_23)[name = tensor("input_115")]; + tensor var_5202 = const()[name = tensor("op_5202"), val = tensor(0x1.4f8b58p-17)]; + tensor input_117_axes_0 = const()[name = tensor("input_117_axes_0"), val = tensor([-1])]; + tensor input_117 = layer_norm(axes = input_117_axes_0, beta = norm11_2_bias, epsilon = var_5202, gamma = norm11_2_weight, x = input_115)[name = tensor("input_117")]; + tensor var_5210 = linear(bias = linear_2_bias_0, weight = linear11_1_weight, x = input_117)[name = tensor("linear_46")]; + tensor input_119_mode_0 = const()[name = tensor("input_119_mode_0"), val = tensor("EXACT")]; + tensor input_119 = gelu(mode = input_119_mode_0, x = var_5210)[name = tensor("input_119")]; + tensor ffn_out_23 = linear(bias = linear_1_bias_0, weight = linear11_2_weight, x = input_119)[name = tensor("linear_47")]; + tensor input_121 = add(x = input_115, y = ffn_out_23)[name = tensor("input_121")]; + tensor var_5219 = const()[name = tensor("op_5219"), val = tensor(0x1.4f8b58p-17)]; + tensor x_25_axes_0 = const()[name = tensor("x_25_axes_0"), val = tensor([-1])]; + tensor x_25 = layer_norm(axes = x_25_axes_0, beta = norm12_1_bias, epsilon = var_5219, gamma = norm12_1_weight, x = input_121)[name = tensor("x_25")]; + tensor var_5251 = linear(bias = linear_0_bias_0, weight = attn12_in_proj_weight, x = x_25)[name = tensor("linear_48")]; + tensor var_5255 = const()[name = tensor("op_5255"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_25 = reshape(shape = var_5255, x = var_5251)[name = tensor("qkv_25")]; + tensor q_73_begin_0 = const()[name = tensor("q_73_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_73_end_0 = const()[name = tensor("q_73_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_73_end_mask_0 = const()[name = tensor("q_73_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_73_squeeze_mask_0 = const()[name = tensor("q_73_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_73 = slice_by_index(begin = q_73_begin_0, end = q_73_end_0, end_mask = q_73_end_mask_0, squeeze_mask = q_73_squeeze_mask_0, x = qkv_25)[name = tensor("q_73")]; + tensor k_49_begin_0 = const()[name = tensor("k_49_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_49_end_0 = const()[name = tensor("k_49_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_49_end_mask_0 = const()[name = tensor("k_49_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_49_squeeze_mask_0 = const()[name = tensor("k_49_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_49 = slice_by_index(begin = k_49_begin_0, end = k_49_end_0, end_mask = k_49_end_mask_0, squeeze_mask = k_49_squeeze_mask_0, x = qkv_25)[name = tensor("k_49")]; + tensor v_25_begin_0 = const()[name = tensor("v_25_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_25_end_0 = const()[name = tensor("v_25_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_25_end_mask_0 = const()[name = tensor("v_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_25_squeeze_mask_0 = const()[name = tensor("v_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_25 = slice_by_index(begin = v_25_begin_0, end = v_25_end_0, end_mask = v_25_end_mask_0, squeeze_mask = v_25_squeeze_mask_0, x = qkv_25)[name = tensor("v_25")]; + tensor freqs_25 = const()[name = tensor("freqs_25"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172742464)))]; + tensor var_5359 = const()[name = tensor("op_5359"), val = tensor([1, 1, 1, 1])]; + tensor ts_77 = reshape(shape = var_5359, x = position12)[name = tensor("ts_77")]; + tensor var_5363 = const()[name = tensor("op_5363"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_25 = reshape(shape = var_5363, x = q_73)[name = tensor("q_complex_25")]; + tensor var_5367 = const()[name = tensor("op_5367"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_25 = reshape(shape = var_5367, x = k_49)[name = tensor("k_complex_25")]; + tensor var_5371_begin_0 = const()[name = tensor("op_5371_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5371_end_0 = const()[name = tensor("op_5371_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5371_end_mask_0 = const()[name = tensor("op_5371_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5371_squeeze_mask_0 = const()[name = tensor("op_5371_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5371 = slice_by_index(begin = var_5371_begin_0, end = var_5371_end_0, end_mask = var_5371_end_mask_0, squeeze_mask = var_5371_squeeze_mask_0, x = q_complex_25)[name = tensor("op_5371")]; + tensor var_5379_begin_0 = const()[name = tensor("op_5379_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5379_end_0 = const()[name = tensor("op_5379_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5379_end_mask_0 = const()[name = tensor("op_5379_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5379_squeeze_mask_0 = const()[name = tensor("op_5379_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5379 = slice_by_index(begin = var_5379_begin_0, end = var_5379_end_0, end_mask = var_5379_end_mask_0, squeeze_mask = var_5379_squeeze_mask_0, x = q_complex_25)[name = tensor("op_5379")]; + tensor var_5387_begin_0 = const()[name = tensor("op_5387_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5387_end_0 = const()[name = tensor("op_5387_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5387_end_mask_0 = const()[name = tensor("op_5387_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5387_squeeze_mask_0 = const()[name = tensor("op_5387_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5387 = slice_by_index(begin = var_5387_begin_0, end = var_5387_end_0, end_mask = var_5387_end_mask_0, squeeze_mask = var_5387_squeeze_mask_0, x = k_complex_25)[name = tensor("op_5387")]; + tensor var_5395_begin_0 = const()[name = tensor("op_5395_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5395_end_0 = const()[name = tensor("op_5395_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5395_end_mask_0 = const()[name = tensor("op_5395_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5395_squeeze_mask_0 = const()[name = tensor("op_5395_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5395 = slice_by_index(begin = var_5395_begin_0, end = var_5395_end_0, end_mask = var_5395_end_mask_0, squeeze_mask = var_5395_squeeze_mask_0, x = k_complex_25)[name = tensor("op_5395")]; + tensor var_5401 = mul(x = freqs_25, y = ts_77)[name = tensor("op_5401")]; + tensor rotr_25 = cos(x = var_5401)[name = tensor("rotr_25")]; + tensor roti_25 = sin(x = var_5401)[name = tensor("roti_25")]; + tensor var_5405 = mul(x = var_5371, y = rotr_25)[name = tensor("op_5405")]; + tensor var_5406 = mul(x = var_5379, y = roti_25)[name = tensor("op_5406")]; + tensor qor_49 = sub(x = var_5405, y = var_5406)[name = tensor("qor_49")]; + tensor var_5409 = mul(x = var_5371, y = roti_25)[name = tensor("op_5409")]; + tensor var_5410 = mul(x = var_5379, y = rotr_25)[name = tensor("op_5410")]; + tensor qoi_49 = add(x = var_5409, y = var_5410)[name = tensor("qoi_49")]; + tensor var_5413 = mul(x = var_5387, y = rotr_25)[name = tensor("op_5413")]; + tensor var_5414 = mul(x = var_5395, y = roti_25)[name = tensor("op_5414")]; + tensor kor_49 = sub(x = var_5413, y = var_5414)[name = tensor("kor_49")]; + tensor var_5417 = mul(x = var_5387, y = roti_25)[name = tensor("op_5417")]; + tensor var_5418 = mul(x = var_5395, y = rotr_25)[name = tensor("op_5418")]; + tensor koi_49 = add(x = var_5417, y = var_5418)[name = tensor("koi_49")]; + tensor qo_25_axis_0 = const()[name = tensor("qo_25_axis_0"), val = tensor(-1)]; + tensor qo_25 = stack(axis = qo_25_axis_0, values = (qor_49, qoi_49))[name = tensor("qo_25")]; + tensor ko_25_axis_0 = const()[name = tensor("ko_25_axis_0"), val = tensor(-1)]; + tensor ko_25 = stack(axis = ko_25_axis_0, values = (kor_49, koi_49))[name = tensor("ko_25")]; + tensor var_5447 = const()[name = tensor("op_5447"), val = tensor([1, 1, 16, 64])]; + tensor q_75 = reshape(shape = var_5447, x = qo_25)[name = tensor("q_75")]; + tensor var_5449 = const()[name = tensor("op_5449"), val = tensor([1, 1, 16, 64])]; + tensor k_51 = reshape(shape = var_5449, x = ko_25)[name = tensor("k_51")]; + tensor _inversed_5471_y_0 = const()[name = tensor("_inversed_5471_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_5471 = mul(x = ts_77, y = _inversed_5471_y_0)[name = tensor("_inversed_5471")]; + tensor var_5472 = floor(x = _inversed_5471)[name = tensor("op_5472")]; + tensor var_5473 = const()[name = tensor("op_5473"), val = tensor(0x1p+9)]; + tensor var_5474 = mul(x = var_5472, y = var_5473)[name = tensor("op_5474")]; + tensor write_indices_float_51 = sub(x = ts_77, y = var_5474)[name = tensor("write_indices_float_51")]; + tensor var_5481_dtype_0 = const()[name = tensor("op_5481_dtype_0"), val = tensor("int32")]; + tensor write_indices_25_reps_0 = const()[name = tensor("write_indices_25_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_5481 = cast(dtype = var_5481_dtype_0, x = write_indices_float_51)[name = tensor("cast_434")]; + tensor write_indices_25 = tile(reps = write_indices_25_reps_0, x = var_5481)[name = tensor("write_indices_25")]; + tensor var_5489_begin_0 = const()[name = tensor("op_5489_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5489_end_0 = const()[name = tensor("op_5489_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_5489_end_mask_0 = const()[name = tensor("op_5489_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5489_squeeze_mask_0 = const()[name = tensor("op_5489_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5489 = slice_by_index(begin = var_5489_begin_0, end = var_5489_end_0, end_mask = var_5489_end_mask_0, squeeze_mask = var_5489_squeeze_mask_0, x = cache12)[name = tensor("op_5489")]; + tensor var_5491_axis_0 = const()[name = tensor("op_5491_axis_0"), val = tensor(1)]; + tensor var_5491_mode_0 = const()[name = tensor("op_5491_mode_0"), val = tensor("update")]; + tensor var_5491_validate_indices_0 = const()[name = tensor("op_5491_validate_indices_0"), val = tensor(false)]; + tensor var_5491 = scatter_along_axis(axis = var_5491_axis_0, data = var_5489, indices = write_indices_25, mode = var_5491_mode_0, updates = k_51, validate_indices = var_5491_validate_indices_0)[name = tensor("op_5491")]; + tensor concat_85 = const()[name = tensor("concat_85"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_86 = const()[name = tensor("concat_86"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_25_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_25_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_70 = const()[name = tensor("shape_70"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_24 = const()[name = tensor("reduce_prod_24"), val = tensor(1048576)]; + tensor range_1d_24_start_0 = const()[name = tensor("range_1d_24_start_0"), val = tensor(0)]; + tensor range_1d_24_step_0 = const()[name = tensor("range_1d_24_step_0"), val = tensor(1)]; + tensor range_1d_24 = range_1d(end = reduce_prod_24, start = range_1d_24_start_0, step = range_1d_24_step_0)[name = tensor("range_1d_24")]; + tensor reshape_120 = reshape(shape = shape_70, x = range_1d_24)[name = tensor("reshape_120")]; + tensor slice_by_index_24 = slice_by_index(begin = concat_85, begin_mask = new_cache_25_internal_tensor_assign_1_begin_mask_0, end = concat_86, end_mask = new_cache_25_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_25_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_25_internal_tensor_assign_1_stride_0, x = reshape_120)[name = tensor("slice_by_index_24")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([-1])]; + tensor reshape_121 = reshape(shape = reshape_121_shape_0, x = slice_by_index_24)[name = tensor("reshape_121")]; + tensor reshape_122_shape_0 = const()[name = tensor("reshape_122_shape_0"), val = tensor([-1])]; + tensor reshape_122 = reshape(shape = reshape_122_shape_0, x = var_5491)[name = tensor("reshape_122")]; + tensor reshape_123_shape_0 = const()[name = tensor("reshape_123_shape_0"), val = tensor([-1])]; + tensor reshape_123 = reshape(shape = reshape_123_shape_0, x = cache12)[name = tensor("reshape_123")]; + tensor scatter_24_mode_0 = const()[name = tensor("scatter_24_mode_0"), val = tensor("update")]; + tensor scatter_24_axis_0 = const()[name = tensor("scatter_24_axis_0"), val = tensor(0)]; + tensor scatter_24_validate_indices_0 = const()[name = tensor("scatter_24_validate_indices_0"), val = tensor(false)]; + tensor scatter_24 = scatter(axis = scatter_24_axis_0, data = reshape_123, indices = reshape_121, mode = scatter_24_mode_0, updates = reshape_122, validate_indices = scatter_24_validate_indices_0)[name = tensor("scatter_24")]; + tensor reshape_124 = reshape(shape = shape_70, x = scatter_24)[name = tensor("reshape_124")]; + tensor var_5499_begin_0 = const()[name = tensor("op_5499_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_5499_end_0 = const()[name = tensor("op_5499_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_5499_end_mask_0 = const()[name = tensor("op_5499_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5499_squeeze_mask_0 = const()[name = tensor("op_5499_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5499 = slice_by_index(begin = var_5499_begin_0, end = var_5499_end_0, end_mask = var_5499_end_mask_0, squeeze_mask = var_5499_squeeze_mask_0, x = reshape_124)[name = tensor("op_5499")]; + tensor var_5501_axis_0 = const()[name = tensor("op_5501_axis_0"), val = tensor(1)]; + tensor var_5501_mode_0 = const()[name = tensor("op_5501_mode_0"), val = tensor("update")]; + tensor var_5501_validate_indices_0 = const()[name = tensor("op_5501_validate_indices_0"), val = tensor(false)]; + tensor var_5501 = scatter_along_axis(axis = var_5501_axis_0, data = var_5499, indices = write_indices_25, mode = var_5501_mode_0, updates = v_25, validate_indices = var_5501_validate_indices_0)[name = tensor("op_5501")]; + tensor concat_87 = const()[name = tensor("concat_87"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_88 = const()[name = tensor("concat_88"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_25_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_25_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_71 = const()[name = tensor("shape_71"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_25 = const()[name = tensor("reduce_prod_25"), val = tensor(1048576)]; + tensor range_1d_25_start_0 = const()[name = tensor("range_1d_25_start_0"), val = tensor(0)]; + tensor range_1d_25_step_0 = const()[name = tensor("range_1d_25_step_0"), val = tensor(1)]; + tensor range_1d_25 = range_1d(end = reduce_prod_25, start = range_1d_25_start_0, step = range_1d_25_step_0)[name = tensor("range_1d_25")]; + tensor reshape_125 = reshape(shape = shape_71, x = range_1d_25)[name = tensor("reshape_125")]; + tensor slice_by_index_25 = slice_by_index(begin = concat_87, begin_mask = new_cache_25_internal_tensor_assign_2_begin_mask_0, end = concat_88, end_mask = new_cache_25_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_25_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_25_internal_tensor_assign_2_stride_0, x = reshape_125)[name = tensor("slice_by_index_25")]; + tensor reshape_126_shape_0 = const()[name = tensor("reshape_126_shape_0"), val = tensor([-1])]; + tensor reshape_126 = reshape(shape = reshape_126_shape_0, x = slice_by_index_25)[name = tensor("reshape_126")]; + tensor reshape_127_shape_0 = const()[name = tensor("reshape_127_shape_0"), val = tensor([-1])]; + tensor reshape_127 = reshape(shape = reshape_127_shape_0, x = var_5501)[name = tensor("reshape_127")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([-1])]; + tensor reshape_128 = reshape(shape = reshape_128_shape_0, x = reshape_124)[name = tensor("reshape_128")]; + tensor scatter_25_mode_0 = const()[name = tensor("scatter_25_mode_0"), val = tensor("update")]; + tensor scatter_25_axis_0 = const()[name = tensor("scatter_25_axis_0"), val = tensor(0)]; + tensor scatter_25_validate_indices_0 = const()[name = tensor("scatter_25_validate_indices_0"), val = tensor(false)]; + tensor scatter_25 = scatter(axis = scatter_25_axis_0, data = reshape_128, indices = reshape_126, mode = scatter_25_mode_0, updates = reshape_127, validate_indices = scatter_25_validate_indices_0)[name = tensor("scatter_25")]; + tensor new_cache_25_internal_tensor_assign_2 = reshape(shape = shape_71, x = scatter_25)[name = tensor("reshape_129")]; + tensor keys_73_begin_0 = const()[name = tensor("keys_73_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_73_end_0 = const()[name = tensor("keys_73_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_73_end_mask_0 = const()[name = tensor("keys_73_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_73_squeeze_mask_0 = const()[name = tensor("keys_73_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_73 = slice_by_index(begin = keys_73_begin_0, end = keys_73_end_0, end_mask = keys_73_end_mask_0, squeeze_mask = keys_73_squeeze_mask_0, x = new_cache_25_internal_tensor_assign_2)[name = tensor("keys_73")]; + tensor values_73_begin_0 = const()[name = tensor("values_73_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_73_end_0 = const()[name = tensor("values_73_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_73_end_mask_0 = const()[name = tensor("values_73_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_73_squeeze_mask_0 = const()[name = tensor("values_73_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_73 = slice_by_index(begin = values_73_begin_0, end = values_73_end_0, end_mask = values_73_end_mask_0, squeeze_mask = values_73_squeeze_mask_0, x = new_cache_25_internal_tensor_assign_2)[name = tensor("values_73")]; + tensor var_5513 = not_equal(x = keys_73, y = keys_73)[name = tensor("op_5513")]; + tensor keys_75 = select(a = var_491, b = keys_73, cond = var_5513)[name = tensor("keys_75")]; + tensor var_5521 = not_equal(x = values_73, y = values_73)[name = tensor("op_5521")]; + tensor values_75 = select(a = var_491, b = values_73, cond = var_5521)[name = tensor("values_75")]; + tensor var_5545 = const()[name = tensor("op_5545"), val = tensor([0, 2, 1, 3])]; + tensor var_5558 = const()[name = tensor("op_5558"), val = tensor([1, 1, 1])]; + tensor var_5559 = reshape(shape = var_5558, x = position12)[name = tensor("op_5559")]; + tensor var_5576 = const()[name = tensor("op_5576"), val = tensor(0x1p+0)]; + tensor valid_len_25 = add(x = var_5559, y = var_5576)[name = tensor("valid_len_25")]; + tensor valid_mask_25 = less(x = k_positions_1_promoted, y = valid_len_25)[name = tensor("valid_mask_25")]; + tensor causal_mask_25 = less_equal(x = k_positions_1_promoted, y = var_5559)[name = tensor("causal_mask_25")]; + tensor attn_mask_49 = logical_and(x = valid_mask_25, y = causal_mask_25)[name = tensor("attn_mask_49")]; + tensor attn_mask_51_axes_0 = const()[name = tensor("attn_mask_51_axes_0"), val = tensor([1])]; + tensor attn_mask_51 = expand_dims(axes = attn_mask_51_axes_0, x = attn_mask_49)[name = tensor("attn_mask_51")]; + tensor var_5588 = const()[name = tensor("op_5588"), val = tensor([0x1.fffe5cp-4])]; + tensor var_5594_transpose_x_0 = const()[name = tensor("op_5594_transpose_x_0"), val = tensor(false)]; + tensor var_5594_transpose_y_0 = const()[name = tensor("op_5594_transpose_y_0"), val = tensor(false)]; + tensor transpose_93_perm_0 = const()[name = tensor("transpose_93_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_94_perm_0 = const()[name = tensor("transpose_94_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = keys_75)[name = tensor("transpose_156")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = q_75)[name = tensor("transpose_157")]; + tensor var_5594 = matmul(transpose_x = var_5594_transpose_x_0, transpose_y = var_5594_transpose_y_0, x = transpose_93, y = transpose_94)[name = tensor("op_5594")]; + tensor attn_weights_73 = mul(x = var_5594, y = var_5588)[name = tensor("attn_weights_73")]; + tensor var_5596 = logical_not(x = attn_mask_51)[name = tensor("op_5596")]; + tensor var_5597 = const()[name = tensor("op_5597"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_75 = select(a = var_5597, b = attn_weights_73, cond = var_5596)[name = tensor("attn_weights_75")]; + tensor var_5599 = const()[name = tensor("op_5599"), val = tensor(-1)]; + tensor attn_weights_77 = softmax(axis = var_5599, x = attn_weights_75)[name = tensor("attn_weights_77")]; + tensor attn_output_25_transpose_x_0 = const()[name = tensor("attn_output_25_transpose_x_0"), val = tensor(false)]; + tensor attn_output_25_transpose_y_0 = const()[name = tensor("attn_output_25_transpose_y_0"), val = tensor(false)]; + tensor values_77 = transpose(perm = var_5545, x = values_75)[name = tensor("transpose_158")]; + tensor attn_output_25 = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = attn_weights_77, y = values_77)[name = tensor("attn_output_25")]; + tensor var_5607 = const()[name = tensor("op_5607"), val = tensor([0, 2, 1, 3])]; + tensor var_5610 = const()[name = tensor("op_5610"), val = tensor([1, 1, 1024])]; + tensor var_5608 = transpose(perm = var_5607, x = attn_output_25)[name = tensor("transpose_155")]; + tensor input_123 = reshape(shape = var_5610, x = var_5608)[name = tensor("input_123")]; + tensor attn_out_25 = linear(bias = linear_1_bias_0, weight = attn12_out_proj_weight, x = input_123)[name = tensor("linear_49")]; + tensor var_5616 = const()[name = tensor("op_5616"), val = tensor(0x1p+0)]; + tensor var_5617 = add(x = position12, y = var_5616)[name = tensor("op_5617")]; + tensor input_125 = add(x = input_121, y = attn_out_25)[name = tensor("input_125")]; + tensor var_5621 = const()[name = tensor("op_5621"), val = tensor(0x1.4f8b58p-17)]; + tensor input_127_axes_0 = const()[name = tensor("input_127_axes_0"), val = tensor([-1])]; + tensor input_127 = layer_norm(axes = input_127_axes_0, beta = norm12_2_bias, epsilon = var_5621, gamma = norm12_2_weight, x = input_125)[name = tensor("input_127")]; + tensor var_5629 = linear(bias = linear_2_bias_0, weight = linear12_1_weight, x = input_127)[name = tensor("linear_50")]; + tensor input_129_mode_0 = const()[name = tensor("input_129_mode_0"), val = tensor("EXACT")]; + tensor input_129 = gelu(mode = input_129_mode_0, x = var_5629)[name = tensor("input_129")]; + tensor ffn_out_25 = linear(bias = linear_1_bias_0, weight = linear12_2_weight, x = input_129)[name = tensor("linear_51")]; + tensor input_131 = add(x = input_125, y = ffn_out_25)[name = tensor("input_131")]; + tensor var_5638 = const()[name = tensor("op_5638"), val = tensor(0x1.4f8b58p-17)]; + tensor x_27_axes_0 = const()[name = tensor("x_27_axes_0"), val = tensor([-1])]; + tensor x_27 = layer_norm(axes = x_27_axes_0, beta = norm13_1_bias, epsilon = var_5638, gamma = norm13_1_weight, x = input_131)[name = tensor("x_27")]; + tensor var_5670 = linear(bias = linear_0_bias_0, weight = attn13_in_proj_weight, x = x_27)[name = tensor("linear_52")]; + tensor var_5674 = const()[name = tensor("op_5674"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_27 = reshape(shape = var_5674, x = var_5670)[name = tensor("qkv_27")]; + tensor q_79_begin_0 = const()[name = tensor("q_79_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_79_end_0 = const()[name = tensor("q_79_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_79_end_mask_0 = const()[name = tensor("q_79_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_79_squeeze_mask_0 = const()[name = tensor("q_79_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_79 = slice_by_index(begin = q_79_begin_0, end = q_79_end_0, end_mask = q_79_end_mask_0, squeeze_mask = q_79_squeeze_mask_0, x = qkv_27)[name = tensor("q_79")]; + tensor k_53_begin_0 = const()[name = tensor("k_53_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_53_end_0 = const()[name = tensor("k_53_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_53_end_mask_0 = const()[name = tensor("k_53_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_53_squeeze_mask_0 = const()[name = tensor("k_53_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_53 = slice_by_index(begin = k_53_begin_0, end = k_53_end_0, end_mask = k_53_end_mask_0, squeeze_mask = k_53_squeeze_mask_0, x = qkv_27)[name = tensor("k_53")]; + tensor v_27_begin_0 = const()[name = tensor("v_27_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_27_end_0 = const()[name = tensor("v_27_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_27_end_mask_0 = const()[name = tensor("v_27_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_27_squeeze_mask_0 = const()[name = tensor("v_27_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_27 = slice_by_index(begin = v_27_begin_0, end = v_27_end_0, end_mask = v_27_end_mask_0, squeeze_mask = v_27_squeeze_mask_0, x = qkv_27)[name = tensor("v_27")]; + tensor freqs_27 = const()[name = tensor("freqs_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172742656)))]; + tensor var_5778 = const()[name = tensor("op_5778"), val = tensor([1, 1, 1, 1])]; + tensor ts_83 = reshape(shape = var_5778, x = position13)[name = tensor("ts_83")]; + tensor var_5782 = const()[name = tensor("op_5782"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_27 = reshape(shape = var_5782, x = q_79)[name = tensor("q_complex_27")]; + tensor var_5786 = const()[name = tensor("op_5786"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_27 = reshape(shape = var_5786, x = k_53)[name = tensor("k_complex_27")]; + tensor var_5790_begin_0 = const()[name = tensor("op_5790_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5790_end_0 = const()[name = tensor("op_5790_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5790_end_mask_0 = const()[name = tensor("op_5790_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5790_squeeze_mask_0 = const()[name = tensor("op_5790_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5790 = slice_by_index(begin = var_5790_begin_0, end = var_5790_end_0, end_mask = var_5790_end_mask_0, squeeze_mask = var_5790_squeeze_mask_0, x = q_complex_27)[name = tensor("op_5790")]; + tensor var_5798_begin_0 = const()[name = tensor("op_5798_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5798_end_0 = const()[name = tensor("op_5798_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5798_end_mask_0 = const()[name = tensor("op_5798_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5798_squeeze_mask_0 = const()[name = tensor("op_5798_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5798 = slice_by_index(begin = var_5798_begin_0, end = var_5798_end_0, end_mask = var_5798_end_mask_0, squeeze_mask = var_5798_squeeze_mask_0, x = q_complex_27)[name = tensor("op_5798")]; + tensor var_5806_begin_0 = const()[name = tensor("op_5806_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5806_end_0 = const()[name = tensor("op_5806_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5806_end_mask_0 = const()[name = tensor("op_5806_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5806_squeeze_mask_0 = const()[name = tensor("op_5806_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5806 = slice_by_index(begin = var_5806_begin_0, end = var_5806_end_0, end_mask = var_5806_end_mask_0, squeeze_mask = var_5806_squeeze_mask_0, x = k_complex_27)[name = tensor("op_5806")]; + tensor var_5814_begin_0 = const()[name = tensor("op_5814_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5814_end_0 = const()[name = tensor("op_5814_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5814_end_mask_0 = const()[name = tensor("op_5814_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5814_squeeze_mask_0 = const()[name = tensor("op_5814_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5814 = slice_by_index(begin = var_5814_begin_0, end = var_5814_end_0, end_mask = var_5814_end_mask_0, squeeze_mask = var_5814_squeeze_mask_0, x = k_complex_27)[name = tensor("op_5814")]; + tensor var_5820 = mul(x = freqs_27, y = ts_83)[name = tensor("op_5820")]; + tensor rotr_27 = cos(x = var_5820)[name = tensor("rotr_27")]; + tensor roti_27 = sin(x = var_5820)[name = tensor("roti_27")]; + tensor var_5824 = mul(x = var_5790, y = rotr_27)[name = tensor("op_5824")]; + tensor var_5825 = mul(x = var_5798, y = roti_27)[name = tensor("op_5825")]; + tensor qor_53 = sub(x = var_5824, y = var_5825)[name = tensor("qor_53")]; + tensor var_5828 = mul(x = var_5790, y = roti_27)[name = tensor("op_5828")]; + tensor var_5829 = mul(x = var_5798, y = rotr_27)[name = tensor("op_5829")]; + tensor qoi_53 = add(x = var_5828, y = var_5829)[name = tensor("qoi_53")]; + tensor var_5832 = mul(x = var_5806, y = rotr_27)[name = tensor("op_5832")]; + tensor var_5833 = mul(x = var_5814, y = roti_27)[name = tensor("op_5833")]; + tensor kor_53 = sub(x = var_5832, y = var_5833)[name = tensor("kor_53")]; + tensor var_5836 = mul(x = var_5806, y = roti_27)[name = tensor("op_5836")]; + tensor var_5837 = mul(x = var_5814, y = rotr_27)[name = tensor("op_5837")]; + tensor koi_53 = add(x = var_5836, y = var_5837)[name = tensor("koi_53")]; + tensor qo_27_axis_0 = const()[name = tensor("qo_27_axis_0"), val = tensor(-1)]; + tensor qo_27 = stack(axis = qo_27_axis_0, values = (qor_53, qoi_53))[name = tensor("qo_27")]; + tensor ko_27_axis_0 = const()[name = tensor("ko_27_axis_0"), val = tensor(-1)]; + tensor ko_27 = stack(axis = ko_27_axis_0, values = (kor_53, koi_53))[name = tensor("ko_27")]; + tensor var_5866 = const()[name = tensor("op_5866"), val = tensor([1, 1, 16, 64])]; + tensor q_81 = reshape(shape = var_5866, x = qo_27)[name = tensor("q_81")]; + tensor var_5868 = const()[name = tensor("op_5868"), val = tensor([1, 1, 16, 64])]; + tensor k_55 = reshape(shape = var_5868, x = ko_27)[name = tensor("k_55")]; + tensor _inversed_5890_y_0 = const()[name = tensor("_inversed_5890_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_5890 = mul(x = ts_83, y = _inversed_5890_y_0)[name = tensor("_inversed_5890")]; + tensor var_5891 = floor(x = _inversed_5890)[name = tensor("op_5891")]; + tensor var_5892 = const()[name = tensor("op_5892"), val = tensor(0x1p+9)]; + tensor var_5893 = mul(x = var_5891, y = var_5892)[name = tensor("op_5893")]; + tensor write_indices_float_55 = sub(x = ts_83, y = var_5893)[name = tensor("write_indices_float_55")]; + tensor var_5900_dtype_0 = const()[name = tensor("op_5900_dtype_0"), val = tensor("int32")]; + tensor write_indices_27_reps_0 = const()[name = tensor("write_indices_27_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_5900 = cast(dtype = var_5900_dtype_0, x = write_indices_float_55)[name = tensor("cast_433")]; + tensor write_indices_27 = tile(reps = write_indices_27_reps_0, x = var_5900)[name = tensor("write_indices_27")]; + tensor var_5908_begin_0 = const()[name = tensor("op_5908_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5908_end_0 = const()[name = tensor("op_5908_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_5908_end_mask_0 = const()[name = tensor("op_5908_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5908_squeeze_mask_0 = const()[name = tensor("op_5908_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5908 = slice_by_index(begin = var_5908_begin_0, end = var_5908_end_0, end_mask = var_5908_end_mask_0, squeeze_mask = var_5908_squeeze_mask_0, x = cache13)[name = tensor("op_5908")]; + tensor var_5910_axis_0 = const()[name = tensor("op_5910_axis_0"), val = tensor(1)]; + tensor var_5910_mode_0 = const()[name = tensor("op_5910_mode_0"), val = tensor("update")]; + tensor var_5910_validate_indices_0 = const()[name = tensor("op_5910_validate_indices_0"), val = tensor(false)]; + tensor var_5910 = scatter_along_axis(axis = var_5910_axis_0, data = var_5908, indices = write_indices_27, mode = var_5910_mode_0, updates = k_55, validate_indices = var_5910_validate_indices_0)[name = tensor("op_5910")]; + tensor concat_92 = const()[name = tensor("concat_92"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_93 = const()[name = tensor("concat_93"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_27_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_27_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_72 = const()[name = tensor("shape_72"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_26 = const()[name = tensor("reduce_prod_26"), val = tensor(1048576)]; + tensor range_1d_26_start_0 = const()[name = tensor("range_1d_26_start_0"), val = tensor(0)]; + tensor range_1d_26_step_0 = const()[name = tensor("range_1d_26_step_0"), val = tensor(1)]; + tensor range_1d_26 = range_1d(end = reduce_prod_26, start = range_1d_26_start_0, step = range_1d_26_step_0)[name = tensor("range_1d_26")]; + tensor reshape_130 = reshape(shape = shape_72, x = range_1d_26)[name = tensor("reshape_130")]; + tensor slice_by_index_26 = slice_by_index(begin = concat_92, begin_mask = new_cache_27_internal_tensor_assign_1_begin_mask_0, end = concat_93, end_mask = new_cache_27_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_27_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_27_internal_tensor_assign_1_stride_0, x = reshape_130)[name = tensor("slice_by_index_26")]; + tensor reshape_131_shape_0 = const()[name = tensor("reshape_131_shape_0"), val = tensor([-1])]; + tensor reshape_131 = reshape(shape = reshape_131_shape_0, x = slice_by_index_26)[name = tensor("reshape_131")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([-1])]; + tensor reshape_132 = reshape(shape = reshape_132_shape_0, x = var_5910)[name = tensor("reshape_132")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([-1])]; + tensor reshape_133 = reshape(shape = reshape_133_shape_0, x = cache13)[name = tensor("reshape_133")]; + tensor scatter_26_mode_0 = const()[name = tensor("scatter_26_mode_0"), val = tensor("update")]; + tensor scatter_26_axis_0 = const()[name = tensor("scatter_26_axis_0"), val = tensor(0)]; + tensor scatter_26_validate_indices_0 = const()[name = tensor("scatter_26_validate_indices_0"), val = tensor(false)]; + tensor scatter_26 = scatter(axis = scatter_26_axis_0, data = reshape_133, indices = reshape_131, mode = scatter_26_mode_0, updates = reshape_132, validate_indices = scatter_26_validate_indices_0)[name = tensor("scatter_26")]; + tensor reshape_134 = reshape(shape = shape_72, x = scatter_26)[name = tensor("reshape_134")]; + tensor var_5918_begin_0 = const()[name = tensor("op_5918_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_5918_end_0 = const()[name = tensor("op_5918_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_5918_end_mask_0 = const()[name = tensor("op_5918_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5918_squeeze_mask_0 = const()[name = tensor("op_5918_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5918 = slice_by_index(begin = var_5918_begin_0, end = var_5918_end_0, end_mask = var_5918_end_mask_0, squeeze_mask = var_5918_squeeze_mask_0, x = reshape_134)[name = tensor("op_5918")]; + tensor var_5920_axis_0 = const()[name = tensor("op_5920_axis_0"), val = tensor(1)]; + tensor var_5920_mode_0 = const()[name = tensor("op_5920_mode_0"), val = tensor("update")]; + tensor var_5920_validate_indices_0 = const()[name = tensor("op_5920_validate_indices_0"), val = tensor(false)]; + tensor var_5920 = scatter_along_axis(axis = var_5920_axis_0, data = var_5918, indices = write_indices_27, mode = var_5920_mode_0, updates = v_27, validate_indices = var_5920_validate_indices_0)[name = tensor("op_5920")]; + tensor concat_94 = const()[name = tensor("concat_94"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_95 = const()[name = tensor("concat_95"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_27_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_27_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_73 = const()[name = tensor("shape_73"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_27 = const()[name = tensor("reduce_prod_27"), val = tensor(1048576)]; + tensor range_1d_27_start_0 = const()[name = tensor("range_1d_27_start_0"), val = tensor(0)]; + tensor range_1d_27_step_0 = const()[name = tensor("range_1d_27_step_0"), val = tensor(1)]; + tensor range_1d_27 = range_1d(end = reduce_prod_27, start = range_1d_27_start_0, step = range_1d_27_step_0)[name = tensor("range_1d_27")]; + tensor reshape_135 = reshape(shape = shape_73, x = range_1d_27)[name = tensor("reshape_135")]; + tensor slice_by_index_27 = slice_by_index(begin = concat_94, begin_mask = new_cache_27_internal_tensor_assign_2_begin_mask_0, end = concat_95, end_mask = new_cache_27_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_27_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_27_internal_tensor_assign_2_stride_0, x = reshape_135)[name = tensor("slice_by_index_27")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([-1])]; + tensor reshape_136 = reshape(shape = reshape_136_shape_0, x = slice_by_index_27)[name = tensor("reshape_136")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([-1])]; + tensor reshape_137 = reshape(shape = reshape_137_shape_0, x = var_5920)[name = tensor("reshape_137")]; + tensor reshape_138_shape_0 = const()[name = tensor("reshape_138_shape_0"), val = tensor([-1])]; + tensor reshape_138 = reshape(shape = reshape_138_shape_0, x = reshape_134)[name = tensor("reshape_138")]; + tensor scatter_27_mode_0 = const()[name = tensor("scatter_27_mode_0"), val = tensor("update")]; + tensor scatter_27_axis_0 = const()[name = tensor("scatter_27_axis_0"), val = tensor(0)]; + tensor scatter_27_validate_indices_0 = const()[name = tensor("scatter_27_validate_indices_0"), val = tensor(false)]; + tensor scatter_27 = scatter(axis = scatter_27_axis_0, data = reshape_138, indices = reshape_136, mode = scatter_27_mode_0, updates = reshape_137, validate_indices = scatter_27_validate_indices_0)[name = tensor("scatter_27")]; + tensor new_cache_27_internal_tensor_assign_2 = reshape(shape = shape_73, x = scatter_27)[name = tensor("reshape_139")]; + tensor keys_79_begin_0 = const()[name = tensor("keys_79_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_79_end_0 = const()[name = tensor("keys_79_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_79_end_mask_0 = const()[name = tensor("keys_79_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_79_squeeze_mask_0 = const()[name = tensor("keys_79_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_79 = slice_by_index(begin = keys_79_begin_0, end = keys_79_end_0, end_mask = keys_79_end_mask_0, squeeze_mask = keys_79_squeeze_mask_0, x = new_cache_27_internal_tensor_assign_2)[name = tensor("keys_79")]; + tensor values_79_begin_0 = const()[name = tensor("values_79_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_79_end_0 = const()[name = tensor("values_79_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_79_end_mask_0 = const()[name = tensor("values_79_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_79_squeeze_mask_0 = const()[name = tensor("values_79_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_79 = slice_by_index(begin = values_79_begin_0, end = values_79_end_0, end_mask = values_79_end_mask_0, squeeze_mask = values_79_squeeze_mask_0, x = new_cache_27_internal_tensor_assign_2)[name = tensor("values_79")]; + tensor var_5932 = not_equal(x = keys_79, y = keys_79)[name = tensor("op_5932")]; + tensor keys_81 = select(a = var_491, b = keys_79, cond = var_5932)[name = tensor("keys_81")]; + tensor var_5940 = not_equal(x = values_79, y = values_79)[name = tensor("op_5940")]; + tensor values_81 = select(a = var_491, b = values_79, cond = var_5940)[name = tensor("values_81")]; + tensor var_5964 = const()[name = tensor("op_5964"), val = tensor([0, 2, 1, 3])]; + tensor var_5977 = const()[name = tensor("op_5977"), val = tensor([1, 1, 1])]; + tensor var_5978 = reshape(shape = var_5977, x = position13)[name = tensor("op_5978")]; + tensor var_5995 = const()[name = tensor("op_5995"), val = tensor(0x1p+0)]; + tensor valid_len_27 = add(x = var_5978, y = var_5995)[name = tensor("valid_len_27")]; + tensor valid_mask_27 = less(x = k_positions_1_promoted, y = valid_len_27)[name = tensor("valid_mask_27")]; + tensor causal_mask_27 = less_equal(x = k_positions_1_promoted, y = var_5978)[name = tensor("causal_mask_27")]; + tensor attn_mask_53 = logical_and(x = valid_mask_27, y = causal_mask_27)[name = tensor("attn_mask_53")]; + tensor attn_mask_55_axes_0 = const()[name = tensor("attn_mask_55_axes_0"), val = tensor([1])]; + tensor attn_mask_55 = expand_dims(axes = attn_mask_55_axes_0, x = attn_mask_53)[name = tensor("attn_mask_55")]; + tensor var_6007 = const()[name = tensor("op_6007"), val = tensor([0x1.fffe5cp-4])]; + tensor var_6013_transpose_x_0 = const()[name = tensor("op_6013_transpose_x_0"), val = tensor(false)]; + tensor var_6013_transpose_y_0 = const()[name = tensor("op_6013_transpose_y_0"), val = tensor(false)]; + tensor transpose_95_perm_0 = const()[name = tensor("transpose_95_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_96_perm_0 = const()[name = tensor("transpose_96_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = keys_81)[name = tensor("transpose_152")]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = q_81)[name = tensor("transpose_153")]; + tensor var_6013 = matmul(transpose_x = var_6013_transpose_x_0, transpose_y = var_6013_transpose_y_0, x = transpose_95, y = transpose_96)[name = tensor("op_6013")]; + tensor attn_weights_79 = mul(x = var_6013, y = var_6007)[name = tensor("attn_weights_79")]; + tensor var_6015 = logical_not(x = attn_mask_55)[name = tensor("op_6015")]; + tensor var_6016 = const()[name = tensor("op_6016"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_81 = select(a = var_6016, b = attn_weights_79, cond = var_6015)[name = tensor("attn_weights_81")]; + tensor var_6018 = const()[name = tensor("op_6018"), val = tensor(-1)]; + tensor attn_weights_83 = softmax(axis = var_6018, x = attn_weights_81)[name = tensor("attn_weights_83")]; + tensor attn_output_27_transpose_x_0 = const()[name = tensor("attn_output_27_transpose_x_0"), val = tensor(false)]; + tensor attn_output_27_transpose_y_0 = const()[name = tensor("attn_output_27_transpose_y_0"), val = tensor(false)]; + tensor values_83 = transpose(perm = var_5964, x = values_81)[name = tensor("transpose_154")]; + tensor attn_output_27 = matmul(transpose_x = attn_output_27_transpose_x_0, transpose_y = attn_output_27_transpose_y_0, x = attn_weights_83, y = values_83)[name = tensor("attn_output_27")]; + tensor var_6026 = const()[name = tensor("op_6026"), val = tensor([0, 2, 1, 3])]; + tensor var_6029 = const()[name = tensor("op_6029"), val = tensor([1, 1, 1024])]; + tensor var_6027 = transpose(perm = var_6026, x = attn_output_27)[name = tensor("transpose_151")]; + tensor input_133 = reshape(shape = var_6029, x = var_6027)[name = tensor("input_133")]; + tensor attn_out_27 = linear(bias = linear_1_bias_0, weight = attn13_out_proj_weight, x = input_133)[name = tensor("linear_53")]; + tensor var_6035 = const()[name = tensor("op_6035"), val = tensor(0x1p+0)]; + tensor var_6036 = add(x = position13, y = var_6035)[name = tensor("op_6036")]; + tensor input_135 = add(x = input_131, y = attn_out_27)[name = tensor("input_135")]; + tensor var_6040 = const()[name = tensor("op_6040"), val = tensor(0x1.4f8b58p-17)]; + tensor input_137_axes_0 = const()[name = tensor("input_137_axes_0"), val = tensor([-1])]; + tensor input_137 = layer_norm(axes = input_137_axes_0, beta = norm13_2_bias, epsilon = var_6040, gamma = norm13_2_weight, x = input_135)[name = tensor("input_137")]; + tensor var_6048 = linear(bias = linear_2_bias_0, weight = linear13_1_weight, x = input_137)[name = tensor("linear_54")]; + tensor input_139_mode_0 = const()[name = tensor("input_139_mode_0"), val = tensor("EXACT")]; + tensor input_139 = gelu(mode = input_139_mode_0, x = var_6048)[name = tensor("input_139")]; + tensor ffn_out_27 = linear(bias = linear_1_bias_0, weight = linear13_2_weight, x = input_139)[name = tensor("linear_55")]; + tensor input_141 = add(x = input_135, y = ffn_out_27)[name = tensor("input_141")]; + tensor var_6057 = const()[name = tensor("op_6057"), val = tensor(0x1.4f8b58p-17)]; + tensor x_29_axes_0 = const()[name = tensor("x_29_axes_0"), val = tensor([-1])]; + tensor x_29 = layer_norm(axes = x_29_axes_0, beta = norm14_1_bias, epsilon = var_6057, gamma = norm14_1_weight, x = input_141)[name = tensor("x_29")]; + tensor var_6089 = linear(bias = linear_0_bias_0, weight = attn14_in_proj_weight, x = x_29)[name = tensor("linear_56")]; + tensor var_6093 = const()[name = tensor("op_6093"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_29 = reshape(shape = var_6093, x = var_6089)[name = tensor("qkv_29")]; + tensor q_85_begin_0 = const()[name = tensor("q_85_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_85_end_0 = const()[name = tensor("q_85_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_85_end_mask_0 = const()[name = tensor("q_85_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_85_squeeze_mask_0 = const()[name = tensor("q_85_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_85 = slice_by_index(begin = q_85_begin_0, end = q_85_end_0, end_mask = q_85_end_mask_0, squeeze_mask = q_85_squeeze_mask_0, x = qkv_29)[name = tensor("q_85")]; + tensor k_57_begin_0 = const()[name = tensor("k_57_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_57_end_0 = const()[name = tensor("k_57_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_57_end_mask_0 = const()[name = tensor("k_57_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_57_squeeze_mask_0 = const()[name = tensor("k_57_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_57 = slice_by_index(begin = k_57_begin_0, end = k_57_end_0, end_mask = k_57_end_mask_0, squeeze_mask = k_57_squeeze_mask_0, x = qkv_29)[name = tensor("k_57")]; + tensor v_29_begin_0 = const()[name = tensor("v_29_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_29_end_0 = const()[name = tensor("v_29_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_29_end_mask_0 = const()[name = tensor("v_29_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_29_squeeze_mask_0 = const()[name = tensor("v_29_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_29 = slice_by_index(begin = v_29_begin_0, end = v_29_end_0, end_mask = v_29_end_mask_0, squeeze_mask = v_29_squeeze_mask_0, x = qkv_29)[name = tensor("v_29")]; + tensor freqs_29 = const()[name = tensor("freqs_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172742848)))]; + tensor var_6197 = const()[name = tensor("op_6197"), val = tensor([1, 1, 1, 1])]; + tensor ts_89 = reshape(shape = var_6197, x = position14)[name = tensor("ts_89")]; + tensor var_6201 = const()[name = tensor("op_6201"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_29 = reshape(shape = var_6201, x = q_85)[name = tensor("q_complex_29")]; + tensor var_6205 = const()[name = tensor("op_6205"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_29 = reshape(shape = var_6205, x = k_57)[name = tensor("k_complex_29")]; + tensor var_6209_begin_0 = const()[name = tensor("op_6209_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6209_end_0 = const()[name = tensor("op_6209_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6209_end_mask_0 = const()[name = tensor("op_6209_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6209_squeeze_mask_0 = const()[name = tensor("op_6209_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6209 = slice_by_index(begin = var_6209_begin_0, end = var_6209_end_0, end_mask = var_6209_end_mask_0, squeeze_mask = var_6209_squeeze_mask_0, x = q_complex_29)[name = tensor("op_6209")]; + tensor var_6217_begin_0 = const()[name = tensor("op_6217_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6217_end_0 = const()[name = tensor("op_6217_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6217_end_mask_0 = const()[name = tensor("op_6217_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6217_squeeze_mask_0 = const()[name = tensor("op_6217_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6217 = slice_by_index(begin = var_6217_begin_0, end = var_6217_end_0, end_mask = var_6217_end_mask_0, squeeze_mask = var_6217_squeeze_mask_0, x = q_complex_29)[name = tensor("op_6217")]; + tensor var_6225_begin_0 = const()[name = tensor("op_6225_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6225_end_0 = const()[name = tensor("op_6225_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6225_end_mask_0 = const()[name = tensor("op_6225_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6225_squeeze_mask_0 = const()[name = tensor("op_6225_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6225 = slice_by_index(begin = var_6225_begin_0, end = var_6225_end_0, end_mask = var_6225_end_mask_0, squeeze_mask = var_6225_squeeze_mask_0, x = k_complex_29)[name = tensor("op_6225")]; + tensor var_6233_begin_0 = const()[name = tensor("op_6233_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6233_end_0 = const()[name = tensor("op_6233_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6233_end_mask_0 = const()[name = tensor("op_6233_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6233_squeeze_mask_0 = const()[name = tensor("op_6233_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6233 = slice_by_index(begin = var_6233_begin_0, end = var_6233_end_0, end_mask = var_6233_end_mask_0, squeeze_mask = var_6233_squeeze_mask_0, x = k_complex_29)[name = tensor("op_6233")]; + tensor var_6239 = mul(x = freqs_29, y = ts_89)[name = tensor("op_6239")]; + tensor rotr_29 = cos(x = var_6239)[name = tensor("rotr_29")]; + tensor roti_29 = sin(x = var_6239)[name = tensor("roti_29")]; + tensor var_6243 = mul(x = var_6209, y = rotr_29)[name = tensor("op_6243")]; + tensor var_6244 = mul(x = var_6217, y = roti_29)[name = tensor("op_6244")]; + tensor qor_57 = sub(x = var_6243, y = var_6244)[name = tensor("qor_57")]; + tensor var_6247 = mul(x = var_6209, y = roti_29)[name = tensor("op_6247")]; + tensor var_6248 = mul(x = var_6217, y = rotr_29)[name = tensor("op_6248")]; + tensor qoi_57 = add(x = var_6247, y = var_6248)[name = tensor("qoi_57")]; + tensor var_6251 = mul(x = var_6225, y = rotr_29)[name = tensor("op_6251")]; + tensor var_6252 = mul(x = var_6233, y = roti_29)[name = tensor("op_6252")]; + tensor kor_57 = sub(x = var_6251, y = var_6252)[name = tensor("kor_57")]; + tensor var_6255 = mul(x = var_6225, y = roti_29)[name = tensor("op_6255")]; + tensor var_6256 = mul(x = var_6233, y = rotr_29)[name = tensor("op_6256")]; + tensor koi_57 = add(x = var_6255, y = var_6256)[name = tensor("koi_57")]; + tensor qo_29_axis_0 = const()[name = tensor("qo_29_axis_0"), val = tensor(-1)]; + tensor qo_29 = stack(axis = qo_29_axis_0, values = (qor_57, qoi_57))[name = tensor("qo_29")]; + tensor ko_29_axis_0 = const()[name = tensor("ko_29_axis_0"), val = tensor(-1)]; + tensor ko_29 = stack(axis = ko_29_axis_0, values = (kor_57, koi_57))[name = tensor("ko_29")]; + tensor var_6285 = const()[name = tensor("op_6285"), val = tensor([1, 1, 16, 64])]; + tensor q_87 = reshape(shape = var_6285, x = qo_29)[name = tensor("q_87")]; + tensor var_6287 = const()[name = tensor("op_6287"), val = tensor([1, 1, 16, 64])]; + tensor k_59 = reshape(shape = var_6287, x = ko_29)[name = tensor("k_59")]; + tensor _inversed_6309_y_0 = const()[name = tensor("_inversed_6309_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_6309 = mul(x = ts_89, y = _inversed_6309_y_0)[name = tensor("_inversed_6309")]; + tensor var_6310 = floor(x = _inversed_6309)[name = tensor("op_6310")]; + tensor var_6311 = const()[name = tensor("op_6311"), val = tensor(0x1p+9)]; + tensor var_6312 = mul(x = var_6310, y = var_6311)[name = tensor("op_6312")]; + tensor write_indices_float_59 = sub(x = ts_89, y = var_6312)[name = tensor("write_indices_float_59")]; + tensor var_6319_dtype_0 = const()[name = tensor("op_6319_dtype_0"), val = tensor("int32")]; + tensor write_indices_29_reps_0 = const()[name = tensor("write_indices_29_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_6319 = cast(dtype = var_6319_dtype_0, x = write_indices_float_59)[name = tensor("cast_432")]; + tensor write_indices_29 = tile(reps = write_indices_29_reps_0, x = var_6319)[name = tensor("write_indices_29")]; + tensor var_6327_begin_0 = const()[name = tensor("op_6327_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6327_end_0 = const()[name = tensor("op_6327_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_6327_end_mask_0 = const()[name = tensor("op_6327_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6327_squeeze_mask_0 = const()[name = tensor("op_6327_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6327 = slice_by_index(begin = var_6327_begin_0, end = var_6327_end_0, end_mask = var_6327_end_mask_0, squeeze_mask = var_6327_squeeze_mask_0, x = cache14)[name = tensor("op_6327")]; + tensor var_6329_axis_0 = const()[name = tensor("op_6329_axis_0"), val = tensor(1)]; + tensor var_6329_mode_0 = const()[name = tensor("op_6329_mode_0"), val = tensor("update")]; + tensor var_6329_validate_indices_0 = const()[name = tensor("op_6329_validate_indices_0"), val = tensor(false)]; + tensor var_6329 = scatter_along_axis(axis = var_6329_axis_0, data = var_6327, indices = write_indices_29, mode = var_6329_mode_0, updates = k_59, validate_indices = var_6329_validate_indices_0)[name = tensor("op_6329")]; + tensor concat_99 = const()[name = tensor("concat_99"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_100 = const()[name = tensor("concat_100"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_29_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_29_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_74 = const()[name = tensor("shape_74"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_28 = const()[name = tensor("reduce_prod_28"), val = tensor(1048576)]; + tensor range_1d_28_start_0 = const()[name = tensor("range_1d_28_start_0"), val = tensor(0)]; + tensor range_1d_28_step_0 = const()[name = tensor("range_1d_28_step_0"), val = tensor(1)]; + tensor range_1d_28 = range_1d(end = reduce_prod_28, start = range_1d_28_start_0, step = range_1d_28_step_0)[name = tensor("range_1d_28")]; + tensor reshape_140 = reshape(shape = shape_74, x = range_1d_28)[name = tensor("reshape_140")]; + tensor slice_by_index_28 = slice_by_index(begin = concat_99, begin_mask = new_cache_29_internal_tensor_assign_1_begin_mask_0, end = concat_100, end_mask = new_cache_29_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_29_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_29_internal_tensor_assign_1_stride_0, x = reshape_140)[name = tensor("slice_by_index_28")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([-1])]; + tensor reshape_141 = reshape(shape = reshape_141_shape_0, x = slice_by_index_28)[name = tensor("reshape_141")]; + tensor reshape_142_shape_0 = const()[name = tensor("reshape_142_shape_0"), val = tensor([-1])]; + tensor reshape_142 = reshape(shape = reshape_142_shape_0, x = var_6329)[name = tensor("reshape_142")]; + tensor reshape_143_shape_0 = const()[name = tensor("reshape_143_shape_0"), val = tensor([-1])]; + tensor reshape_143 = reshape(shape = reshape_143_shape_0, x = cache14)[name = tensor("reshape_143")]; + tensor scatter_28_mode_0 = const()[name = tensor("scatter_28_mode_0"), val = tensor("update")]; + tensor scatter_28_axis_0 = const()[name = tensor("scatter_28_axis_0"), val = tensor(0)]; + tensor scatter_28_validate_indices_0 = const()[name = tensor("scatter_28_validate_indices_0"), val = tensor(false)]; + tensor scatter_28 = scatter(axis = scatter_28_axis_0, data = reshape_143, indices = reshape_141, mode = scatter_28_mode_0, updates = reshape_142, validate_indices = scatter_28_validate_indices_0)[name = tensor("scatter_28")]; + tensor reshape_144 = reshape(shape = shape_74, x = scatter_28)[name = tensor("reshape_144")]; + tensor var_6337_begin_0 = const()[name = tensor("op_6337_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_6337_end_0 = const()[name = tensor("op_6337_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_6337_end_mask_0 = const()[name = tensor("op_6337_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6337_squeeze_mask_0 = const()[name = tensor("op_6337_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6337 = slice_by_index(begin = var_6337_begin_0, end = var_6337_end_0, end_mask = var_6337_end_mask_0, squeeze_mask = var_6337_squeeze_mask_0, x = reshape_144)[name = tensor("op_6337")]; + tensor var_6339_axis_0 = const()[name = tensor("op_6339_axis_0"), val = tensor(1)]; + tensor var_6339_mode_0 = const()[name = tensor("op_6339_mode_0"), val = tensor("update")]; + tensor var_6339_validate_indices_0 = const()[name = tensor("op_6339_validate_indices_0"), val = tensor(false)]; + tensor var_6339 = scatter_along_axis(axis = var_6339_axis_0, data = var_6337, indices = write_indices_29, mode = var_6339_mode_0, updates = v_29, validate_indices = var_6339_validate_indices_0)[name = tensor("op_6339")]; + tensor concat_101 = const()[name = tensor("concat_101"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_102 = const()[name = tensor("concat_102"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_29_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_29_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_75 = const()[name = tensor("shape_75"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_29 = const()[name = tensor("reduce_prod_29"), val = tensor(1048576)]; + tensor range_1d_29_start_0 = const()[name = tensor("range_1d_29_start_0"), val = tensor(0)]; + tensor range_1d_29_step_0 = const()[name = tensor("range_1d_29_step_0"), val = tensor(1)]; + tensor range_1d_29 = range_1d(end = reduce_prod_29, start = range_1d_29_start_0, step = range_1d_29_step_0)[name = tensor("range_1d_29")]; + tensor reshape_145 = reshape(shape = shape_75, x = range_1d_29)[name = tensor("reshape_145")]; + tensor slice_by_index_29 = slice_by_index(begin = concat_101, begin_mask = new_cache_29_internal_tensor_assign_2_begin_mask_0, end = concat_102, end_mask = new_cache_29_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_29_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_29_internal_tensor_assign_2_stride_0, x = reshape_145)[name = tensor("slice_by_index_29")]; + tensor reshape_146_shape_0 = const()[name = tensor("reshape_146_shape_0"), val = tensor([-1])]; + tensor reshape_146 = reshape(shape = reshape_146_shape_0, x = slice_by_index_29)[name = tensor("reshape_146")]; + tensor reshape_147_shape_0 = const()[name = tensor("reshape_147_shape_0"), val = tensor([-1])]; + tensor reshape_147 = reshape(shape = reshape_147_shape_0, x = var_6339)[name = tensor("reshape_147")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([-1])]; + tensor reshape_148 = reshape(shape = reshape_148_shape_0, x = reshape_144)[name = tensor("reshape_148")]; + tensor scatter_29_mode_0 = const()[name = tensor("scatter_29_mode_0"), val = tensor("update")]; + tensor scatter_29_axis_0 = const()[name = tensor("scatter_29_axis_0"), val = tensor(0)]; + tensor scatter_29_validate_indices_0 = const()[name = tensor("scatter_29_validate_indices_0"), val = tensor(false)]; + tensor scatter_29 = scatter(axis = scatter_29_axis_0, data = reshape_148, indices = reshape_146, mode = scatter_29_mode_0, updates = reshape_147, validate_indices = scatter_29_validate_indices_0)[name = tensor("scatter_29")]; + tensor new_cache_29_internal_tensor_assign_2 = reshape(shape = shape_75, x = scatter_29)[name = tensor("reshape_149")]; + tensor keys_85_begin_0 = const()[name = tensor("keys_85_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_85_end_0 = const()[name = tensor("keys_85_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_85_end_mask_0 = const()[name = tensor("keys_85_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_85_squeeze_mask_0 = const()[name = tensor("keys_85_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_85 = slice_by_index(begin = keys_85_begin_0, end = keys_85_end_0, end_mask = keys_85_end_mask_0, squeeze_mask = keys_85_squeeze_mask_0, x = new_cache_29_internal_tensor_assign_2)[name = tensor("keys_85")]; + tensor values_85_begin_0 = const()[name = tensor("values_85_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_85_end_0 = const()[name = tensor("values_85_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_85_end_mask_0 = const()[name = tensor("values_85_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_85_squeeze_mask_0 = const()[name = tensor("values_85_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_85 = slice_by_index(begin = values_85_begin_0, end = values_85_end_0, end_mask = values_85_end_mask_0, squeeze_mask = values_85_squeeze_mask_0, x = new_cache_29_internal_tensor_assign_2)[name = tensor("values_85")]; + tensor var_6351 = not_equal(x = keys_85, y = keys_85)[name = tensor("op_6351")]; + tensor keys_87 = select(a = var_491, b = keys_85, cond = var_6351)[name = tensor("keys_87")]; + tensor var_6359 = not_equal(x = values_85, y = values_85)[name = tensor("op_6359")]; + tensor values_87 = select(a = var_491, b = values_85, cond = var_6359)[name = tensor("values_87")]; + tensor var_6383 = const()[name = tensor("op_6383"), val = tensor([0, 2, 1, 3])]; + tensor var_6396 = const()[name = tensor("op_6396"), val = tensor([1, 1, 1])]; + tensor var_6397 = reshape(shape = var_6396, x = position14)[name = tensor("op_6397")]; + tensor var_6414 = const()[name = tensor("op_6414"), val = tensor(0x1p+0)]; + tensor valid_len_29 = add(x = var_6397, y = var_6414)[name = tensor("valid_len_29")]; + tensor valid_mask_29 = less(x = k_positions_1_promoted, y = valid_len_29)[name = tensor("valid_mask_29")]; + tensor causal_mask_29 = less_equal(x = k_positions_1_promoted, y = var_6397)[name = tensor("causal_mask_29")]; + tensor attn_mask_57 = logical_and(x = valid_mask_29, y = causal_mask_29)[name = tensor("attn_mask_57")]; + tensor attn_mask_59_axes_0 = const()[name = tensor("attn_mask_59_axes_0"), val = tensor([1])]; + tensor attn_mask_59 = expand_dims(axes = attn_mask_59_axes_0, x = attn_mask_57)[name = tensor("attn_mask_59")]; + tensor var_6426 = const()[name = tensor("op_6426"), val = tensor([0x1.fffe5cp-4])]; + tensor var_6432_transpose_x_0 = const()[name = tensor("op_6432_transpose_x_0"), val = tensor(false)]; + tensor var_6432_transpose_y_0 = const()[name = tensor("op_6432_transpose_y_0"), val = tensor(false)]; + tensor transpose_97_perm_0 = const()[name = tensor("transpose_97_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_98_perm_0 = const()[name = tensor("transpose_98_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = keys_87)[name = tensor("transpose_148")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = q_87)[name = tensor("transpose_149")]; + tensor var_6432 = matmul(transpose_x = var_6432_transpose_x_0, transpose_y = var_6432_transpose_y_0, x = transpose_97, y = transpose_98)[name = tensor("op_6432")]; + tensor attn_weights_85 = mul(x = var_6432, y = var_6426)[name = tensor("attn_weights_85")]; + tensor var_6434 = logical_not(x = attn_mask_59)[name = tensor("op_6434")]; + tensor var_6435 = const()[name = tensor("op_6435"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_87 = select(a = var_6435, b = attn_weights_85, cond = var_6434)[name = tensor("attn_weights_87")]; + tensor var_6437 = const()[name = tensor("op_6437"), val = tensor(-1)]; + tensor attn_weights_89 = softmax(axis = var_6437, x = attn_weights_87)[name = tensor("attn_weights_89")]; + tensor attn_output_29_transpose_x_0 = const()[name = tensor("attn_output_29_transpose_x_0"), val = tensor(false)]; + tensor attn_output_29_transpose_y_0 = const()[name = tensor("attn_output_29_transpose_y_0"), val = tensor(false)]; + tensor values_89 = transpose(perm = var_6383, x = values_87)[name = tensor("transpose_150")]; + tensor attn_output_29 = matmul(transpose_x = attn_output_29_transpose_x_0, transpose_y = attn_output_29_transpose_y_0, x = attn_weights_89, y = values_89)[name = tensor("attn_output_29")]; + tensor var_6445 = const()[name = tensor("op_6445"), val = tensor([0, 2, 1, 3])]; + tensor var_6448 = const()[name = tensor("op_6448"), val = tensor([1, 1, 1024])]; + tensor var_6446 = transpose(perm = var_6445, x = attn_output_29)[name = tensor("transpose_147")]; + tensor input_143 = reshape(shape = var_6448, x = var_6446)[name = tensor("input_143")]; + tensor attn_out_29 = linear(bias = linear_1_bias_0, weight = attn14_out_proj_weight, x = input_143)[name = tensor("linear_57")]; + tensor var_6454 = const()[name = tensor("op_6454"), val = tensor(0x1p+0)]; + tensor var_6455 = add(x = position14, y = var_6454)[name = tensor("op_6455")]; + tensor input_145 = add(x = input_141, y = attn_out_29)[name = tensor("input_145")]; + tensor var_6459 = const()[name = tensor("op_6459"), val = tensor(0x1.4f8b58p-17)]; + tensor input_147_axes_0 = const()[name = tensor("input_147_axes_0"), val = tensor([-1])]; + tensor input_147 = layer_norm(axes = input_147_axes_0, beta = norm14_2_bias, epsilon = var_6459, gamma = norm14_2_weight, x = input_145)[name = tensor("input_147")]; + tensor var_6467 = linear(bias = linear_2_bias_0, weight = linear14_1_weight, x = input_147)[name = tensor("linear_58")]; + tensor input_149_mode_0 = const()[name = tensor("input_149_mode_0"), val = tensor("EXACT")]; + tensor input_149 = gelu(mode = input_149_mode_0, x = var_6467)[name = tensor("input_149")]; + tensor ffn_out_29 = linear(bias = linear_1_bias_0, weight = linear14_2_weight, x = input_149)[name = tensor("linear_59")]; + tensor input_151 = add(x = input_145, y = ffn_out_29)[name = tensor("input_151")]; + tensor var_6476 = const()[name = tensor("op_6476"), val = tensor(0x1.4f8b58p-17)]; + tensor x_31_axes_0 = const()[name = tensor("x_31_axes_0"), val = tensor([-1])]; + tensor x_31 = layer_norm(axes = x_31_axes_0, beta = norm15_1_bias, epsilon = var_6476, gamma = norm15_1_weight, x = input_151)[name = tensor("x_31")]; + tensor var_6508 = linear(bias = linear_0_bias_0, weight = attn15_in_proj_weight, x = x_31)[name = tensor("linear_60")]; + tensor var_6512 = const()[name = tensor("op_6512"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_31 = reshape(shape = var_6512, x = var_6508)[name = tensor("qkv_31")]; + tensor q_91_begin_0 = const()[name = tensor("q_91_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_91_end_0 = const()[name = tensor("q_91_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_91_end_mask_0 = const()[name = tensor("q_91_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_91_squeeze_mask_0 = const()[name = tensor("q_91_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_91 = slice_by_index(begin = q_91_begin_0, end = q_91_end_0, end_mask = q_91_end_mask_0, squeeze_mask = q_91_squeeze_mask_0, x = qkv_31)[name = tensor("q_91")]; + tensor k_61_begin_0 = const()[name = tensor("k_61_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_61_end_0 = const()[name = tensor("k_61_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_61_end_mask_0 = const()[name = tensor("k_61_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_61_squeeze_mask_0 = const()[name = tensor("k_61_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_61 = slice_by_index(begin = k_61_begin_0, end = k_61_end_0, end_mask = k_61_end_mask_0, squeeze_mask = k_61_squeeze_mask_0, x = qkv_31)[name = tensor("k_61")]; + tensor v_31_begin_0 = const()[name = tensor("v_31_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_31_end_0 = const()[name = tensor("v_31_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_31_end_mask_0 = const()[name = tensor("v_31_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_31_squeeze_mask_0 = const()[name = tensor("v_31_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_31 = slice_by_index(begin = v_31_begin_0, end = v_31_end_0, end_mask = v_31_end_mask_0, squeeze_mask = v_31_squeeze_mask_0, x = qkv_31)[name = tensor("v_31")]; + tensor freqs_31 = const()[name = tensor("freqs_31"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172743040)))]; + tensor var_6616 = const()[name = tensor("op_6616"), val = tensor([1, 1, 1, 1])]; + tensor ts_95 = reshape(shape = var_6616, x = position15)[name = tensor("ts_95")]; + tensor var_6620 = const()[name = tensor("op_6620"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_31 = reshape(shape = var_6620, x = q_91)[name = tensor("q_complex_31")]; + tensor var_6624 = const()[name = tensor("op_6624"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_31 = reshape(shape = var_6624, x = k_61)[name = tensor("k_complex_31")]; + tensor var_6628_begin_0 = const()[name = tensor("op_6628_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6628_end_0 = const()[name = tensor("op_6628_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6628_end_mask_0 = const()[name = tensor("op_6628_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6628_squeeze_mask_0 = const()[name = tensor("op_6628_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6628 = slice_by_index(begin = var_6628_begin_0, end = var_6628_end_0, end_mask = var_6628_end_mask_0, squeeze_mask = var_6628_squeeze_mask_0, x = q_complex_31)[name = tensor("op_6628")]; + tensor var_6636_begin_0 = const()[name = tensor("op_6636_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6636_end_0 = const()[name = tensor("op_6636_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6636_end_mask_0 = const()[name = tensor("op_6636_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6636_squeeze_mask_0 = const()[name = tensor("op_6636_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6636 = slice_by_index(begin = var_6636_begin_0, end = var_6636_end_0, end_mask = var_6636_end_mask_0, squeeze_mask = var_6636_squeeze_mask_0, x = q_complex_31)[name = tensor("op_6636")]; + tensor var_6644_begin_0 = const()[name = tensor("op_6644_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6644_end_0 = const()[name = tensor("op_6644_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6644_end_mask_0 = const()[name = tensor("op_6644_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6644_squeeze_mask_0 = const()[name = tensor("op_6644_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6644 = slice_by_index(begin = var_6644_begin_0, end = var_6644_end_0, end_mask = var_6644_end_mask_0, squeeze_mask = var_6644_squeeze_mask_0, x = k_complex_31)[name = tensor("op_6644")]; + tensor var_6652_begin_0 = const()[name = tensor("op_6652_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6652_end_0 = const()[name = tensor("op_6652_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6652_end_mask_0 = const()[name = tensor("op_6652_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6652_squeeze_mask_0 = const()[name = tensor("op_6652_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6652 = slice_by_index(begin = var_6652_begin_0, end = var_6652_end_0, end_mask = var_6652_end_mask_0, squeeze_mask = var_6652_squeeze_mask_0, x = k_complex_31)[name = tensor("op_6652")]; + tensor var_6658 = mul(x = freqs_31, y = ts_95)[name = tensor("op_6658")]; + tensor rotr_31 = cos(x = var_6658)[name = tensor("rotr_31")]; + tensor roti_31 = sin(x = var_6658)[name = tensor("roti_31")]; + tensor var_6662 = mul(x = var_6628, y = rotr_31)[name = tensor("op_6662")]; + tensor var_6663 = mul(x = var_6636, y = roti_31)[name = tensor("op_6663")]; + tensor qor_61 = sub(x = var_6662, y = var_6663)[name = tensor("qor_61")]; + tensor var_6666 = mul(x = var_6628, y = roti_31)[name = tensor("op_6666")]; + tensor var_6667 = mul(x = var_6636, y = rotr_31)[name = tensor("op_6667")]; + tensor qoi_61 = add(x = var_6666, y = var_6667)[name = tensor("qoi_61")]; + tensor var_6670 = mul(x = var_6644, y = rotr_31)[name = tensor("op_6670")]; + tensor var_6671 = mul(x = var_6652, y = roti_31)[name = tensor("op_6671")]; + tensor kor_61 = sub(x = var_6670, y = var_6671)[name = tensor("kor_61")]; + tensor var_6674 = mul(x = var_6644, y = roti_31)[name = tensor("op_6674")]; + tensor var_6675 = mul(x = var_6652, y = rotr_31)[name = tensor("op_6675")]; + tensor koi_61 = add(x = var_6674, y = var_6675)[name = tensor("koi_61")]; + tensor qo_31_axis_0 = const()[name = tensor("qo_31_axis_0"), val = tensor(-1)]; + tensor qo_31 = stack(axis = qo_31_axis_0, values = (qor_61, qoi_61))[name = tensor("qo_31")]; + tensor ko_31_axis_0 = const()[name = tensor("ko_31_axis_0"), val = tensor(-1)]; + tensor ko_31 = stack(axis = ko_31_axis_0, values = (kor_61, koi_61))[name = tensor("ko_31")]; + tensor var_6704 = const()[name = tensor("op_6704"), val = tensor([1, 1, 16, 64])]; + tensor q_93 = reshape(shape = var_6704, x = qo_31)[name = tensor("q_93")]; + tensor var_6706 = const()[name = tensor("op_6706"), val = tensor([1, 1, 16, 64])]; + tensor k_63 = reshape(shape = var_6706, x = ko_31)[name = tensor("k_63")]; + tensor _inversed_6728_y_0 = const()[name = tensor("_inversed_6728_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_6728 = mul(x = ts_95, y = _inversed_6728_y_0)[name = tensor("_inversed_6728")]; + tensor var_6729 = floor(x = _inversed_6728)[name = tensor("op_6729")]; + tensor var_6730 = const()[name = tensor("op_6730"), val = tensor(0x1p+9)]; + tensor var_6731 = mul(x = var_6729, y = var_6730)[name = tensor("op_6731")]; + tensor write_indices_float_63 = sub(x = ts_95, y = var_6731)[name = tensor("write_indices_float_63")]; + tensor var_6738_dtype_0 = const()[name = tensor("op_6738_dtype_0"), val = tensor("int32")]; + tensor write_indices_31_reps_0 = const()[name = tensor("write_indices_31_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_6738 = cast(dtype = var_6738_dtype_0, x = write_indices_float_63)[name = tensor("cast_431")]; + tensor write_indices_31 = tile(reps = write_indices_31_reps_0, x = var_6738)[name = tensor("write_indices_31")]; + tensor var_6746_begin_0 = const()[name = tensor("op_6746_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6746_end_0 = const()[name = tensor("op_6746_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_6746_end_mask_0 = const()[name = tensor("op_6746_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6746_squeeze_mask_0 = const()[name = tensor("op_6746_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6746 = slice_by_index(begin = var_6746_begin_0, end = var_6746_end_0, end_mask = var_6746_end_mask_0, squeeze_mask = var_6746_squeeze_mask_0, x = cache15)[name = tensor("op_6746")]; + tensor var_6748_axis_0 = const()[name = tensor("op_6748_axis_0"), val = tensor(1)]; + tensor var_6748_mode_0 = const()[name = tensor("op_6748_mode_0"), val = tensor("update")]; + tensor var_6748_validate_indices_0 = const()[name = tensor("op_6748_validate_indices_0"), val = tensor(false)]; + tensor var_6748 = scatter_along_axis(axis = var_6748_axis_0, data = var_6746, indices = write_indices_31, mode = var_6748_mode_0, updates = k_63, validate_indices = var_6748_validate_indices_0)[name = tensor("op_6748")]; + tensor concat_106 = const()[name = tensor("concat_106"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_107 = const()[name = tensor("concat_107"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_31_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_31_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_76 = const()[name = tensor("shape_76"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_30 = const()[name = tensor("reduce_prod_30"), val = tensor(1048576)]; + tensor range_1d_30_start_0 = const()[name = tensor("range_1d_30_start_0"), val = tensor(0)]; + tensor range_1d_30_step_0 = const()[name = tensor("range_1d_30_step_0"), val = tensor(1)]; + tensor range_1d_30 = range_1d(end = reduce_prod_30, start = range_1d_30_start_0, step = range_1d_30_step_0)[name = tensor("range_1d_30")]; + tensor reshape_150 = reshape(shape = shape_76, x = range_1d_30)[name = tensor("reshape_150")]; + tensor slice_by_index_30 = slice_by_index(begin = concat_106, begin_mask = new_cache_31_internal_tensor_assign_1_begin_mask_0, end = concat_107, end_mask = new_cache_31_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_31_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_31_internal_tensor_assign_1_stride_0, x = reshape_150)[name = tensor("slice_by_index_30")]; + tensor reshape_151_shape_0 = const()[name = tensor("reshape_151_shape_0"), val = tensor([-1])]; + tensor reshape_151 = reshape(shape = reshape_151_shape_0, x = slice_by_index_30)[name = tensor("reshape_151")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([-1])]; + tensor reshape_152 = reshape(shape = reshape_152_shape_0, x = var_6748)[name = tensor("reshape_152")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([-1])]; + tensor reshape_153 = reshape(shape = reshape_153_shape_0, x = cache15)[name = tensor("reshape_153")]; + tensor scatter_30_mode_0 = const()[name = tensor("scatter_30_mode_0"), val = tensor("update")]; + tensor scatter_30_axis_0 = const()[name = tensor("scatter_30_axis_0"), val = tensor(0)]; + tensor scatter_30_validate_indices_0 = const()[name = tensor("scatter_30_validate_indices_0"), val = tensor(false)]; + tensor scatter_30 = scatter(axis = scatter_30_axis_0, data = reshape_153, indices = reshape_151, mode = scatter_30_mode_0, updates = reshape_152, validate_indices = scatter_30_validate_indices_0)[name = tensor("scatter_30")]; + tensor reshape_154 = reshape(shape = shape_76, x = scatter_30)[name = tensor("reshape_154")]; + tensor var_6756_begin_0 = const()[name = tensor("op_6756_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_6756_end_0 = const()[name = tensor("op_6756_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_6756_end_mask_0 = const()[name = tensor("op_6756_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6756_squeeze_mask_0 = const()[name = tensor("op_6756_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6756 = slice_by_index(begin = var_6756_begin_0, end = var_6756_end_0, end_mask = var_6756_end_mask_0, squeeze_mask = var_6756_squeeze_mask_0, x = reshape_154)[name = tensor("op_6756")]; + tensor var_6758_axis_0 = const()[name = tensor("op_6758_axis_0"), val = tensor(1)]; + tensor var_6758_mode_0 = const()[name = tensor("op_6758_mode_0"), val = tensor("update")]; + tensor var_6758_validate_indices_0 = const()[name = tensor("op_6758_validate_indices_0"), val = tensor(false)]; + tensor var_6758 = scatter_along_axis(axis = var_6758_axis_0, data = var_6756, indices = write_indices_31, mode = var_6758_mode_0, updates = v_31, validate_indices = var_6758_validate_indices_0)[name = tensor("op_6758")]; + tensor concat_108 = const()[name = tensor("concat_108"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_109 = const()[name = tensor("concat_109"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_31_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_31_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_77 = const()[name = tensor("shape_77"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_31 = const()[name = tensor("reduce_prod_31"), val = tensor(1048576)]; + tensor range_1d_31_start_0 = const()[name = tensor("range_1d_31_start_0"), val = tensor(0)]; + tensor range_1d_31_step_0 = const()[name = tensor("range_1d_31_step_0"), val = tensor(1)]; + tensor range_1d_31 = range_1d(end = reduce_prod_31, start = range_1d_31_start_0, step = range_1d_31_step_0)[name = tensor("range_1d_31")]; + tensor reshape_155 = reshape(shape = shape_77, x = range_1d_31)[name = tensor("reshape_155")]; + tensor slice_by_index_31 = slice_by_index(begin = concat_108, begin_mask = new_cache_31_internal_tensor_assign_2_begin_mask_0, end = concat_109, end_mask = new_cache_31_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_31_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_31_internal_tensor_assign_2_stride_0, x = reshape_155)[name = tensor("slice_by_index_31")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([-1])]; + tensor reshape_156 = reshape(shape = reshape_156_shape_0, x = slice_by_index_31)[name = tensor("reshape_156")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([-1])]; + tensor reshape_157 = reshape(shape = reshape_157_shape_0, x = var_6758)[name = tensor("reshape_157")]; + tensor reshape_158_shape_0 = const()[name = tensor("reshape_158_shape_0"), val = tensor([-1])]; + tensor reshape_158 = reshape(shape = reshape_158_shape_0, x = reshape_154)[name = tensor("reshape_158")]; + tensor scatter_31_mode_0 = const()[name = tensor("scatter_31_mode_0"), val = tensor("update")]; + tensor scatter_31_axis_0 = const()[name = tensor("scatter_31_axis_0"), val = tensor(0)]; + tensor scatter_31_validate_indices_0 = const()[name = tensor("scatter_31_validate_indices_0"), val = tensor(false)]; + tensor scatter_31 = scatter(axis = scatter_31_axis_0, data = reshape_158, indices = reshape_156, mode = scatter_31_mode_0, updates = reshape_157, validate_indices = scatter_31_validate_indices_0)[name = tensor("scatter_31")]; + tensor new_cache_31_internal_tensor_assign_2 = reshape(shape = shape_77, x = scatter_31)[name = tensor("reshape_159")]; + tensor keys_91_begin_0 = const()[name = tensor("keys_91_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_91_end_0 = const()[name = tensor("keys_91_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_91_end_mask_0 = const()[name = tensor("keys_91_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_91_squeeze_mask_0 = const()[name = tensor("keys_91_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_91 = slice_by_index(begin = keys_91_begin_0, end = keys_91_end_0, end_mask = keys_91_end_mask_0, squeeze_mask = keys_91_squeeze_mask_0, x = new_cache_31_internal_tensor_assign_2)[name = tensor("keys_91")]; + tensor values_91_begin_0 = const()[name = tensor("values_91_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_91_end_0 = const()[name = tensor("values_91_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_91_end_mask_0 = const()[name = tensor("values_91_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_91_squeeze_mask_0 = const()[name = tensor("values_91_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_91 = slice_by_index(begin = values_91_begin_0, end = values_91_end_0, end_mask = values_91_end_mask_0, squeeze_mask = values_91_squeeze_mask_0, x = new_cache_31_internal_tensor_assign_2)[name = tensor("values_91")]; + tensor var_6770 = not_equal(x = keys_91, y = keys_91)[name = tensor("op_6770")]; + tensor keys_93 = select(a = var_491, b = keys_91, cond = var_6770)[name = tensor("keys_93")]; + tensor var_6778 = not_equal(x = values_91, y = values_91)[name = tensor("op_6778")]; + tensor values_93 = select(a = var_491, b = values_91, cond = var_6778)[name = tensor("values_93")]; + tensor var_6802 = const()[name = tensor("op_6802"), val = tensor([0, 2, 1, 3])]; + tensor var_6815 = const()[name = tensor("op_6815"), val = tensor([1, 1, 1])]; + tensor var_6816 = reshape(shape = var_6815, x = position15)[name = tensor("op_6816")]; + tensor var_6833 = const()[name = tensor("op_6833"), val = tensor(0x1p+0)]; + tensor valid_len_31 = add(x = var_6816, y = var_6833)[name = tensor("valid_len_31")]; + tensor valid_mask_31 = less(x = k_positions_1_promoted, y = valid_len_31)[name = tensor("valid_mask_31")]; + tensor causal_mask_31 = less_equal(x = k_positions_1_promoted, y = var_6816)[name = tensor("causal_mask_31")]; + tensor attn_mask_61 = logical_and(x = valid_mask_31, y = causal_mask_31)[name = tensor("attn_mask_61")]; + tensor attn_mask_63_axes_0 = const()[name = tensor("attn_mask_63_axes_0"), val = tensor([1])]; + tensor attn_mask_63 = expand_dims(axes = attn_mask_63_axes_0, x = attn_mask_61)[name = tensor("attn_mask_63")]; + tensor var_6845 = const()[name = tensor("op_6845"), val = tensor([0x1.fffe5cp-4])]; + tensor var_6851_transpose_x_0 = const()[name = tensor("op_6851_transpose_x_0"), val = tensor(false)]; + tensor var_6851_transpose_y_0 = const()[name = tensor("op_6851_transpose_y_0"), val = tensor(false)]; + tensor transpose_99_perm_0 = const()[name = tensor("transpose_99_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_100_perm_0 = const()[name = tensor("transpose_100_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = keys_93)[name = tensor("transpose_144")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = q_93)[name = tensor("transpose_145")]; + tensor var_6851 = matmul(transpose_x = var_6851_transpose_x_0, transpose_y = var_6851_transpose_y_0, x = transpose_99, y = transpose_100)[name = tensor("op_6851")]; + tensor attn_weights_91 = mul(x = var_6851, y = var_6845)[name = tensor("attn_weights_91")]; + tensor var_6853 = logical_not(x = attn_mask_63)[name = tensor("op_6853")]; + tensor var_6854 = const()[name = tensor("op_6854"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_93 = select(a = var_6854, b = attn_weights_91, cond = var_6853)[name = tensor("attn_weights_93")]; + tensor var_6856 = const()[name = tensor("op_6856"), val = tensor(-1)]; + tensor attn_weights_95 = softmax(axis = var_6856, x = attn_weights_93)[name = tensor("attn_weights_95")]; + tensor attn_output_31_transpose_x_0 = const()[name = tensor("attn_output_31_transpose_x_0"), val = tensor(false)]; + tensor attn_output_31_transpose_y_0 = const()[name = tensor("attn_output_31_transpose_y_0"), val = tensor(false)]; + tensor values_95 = transpose(perm = var_6802, x = values_93)[name = tensor("transpose_146")]; + tensor attn_output_31 = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = attn_weights_95, y = values_95)[name = tensor("attn_output_31")]; + tensor var_6864 = const()[name = tensor("op_6864"), val = tensor([0, 2, 1, 3])]; + tensor var_6867 = const()[name = tensor("op_6867"), val = tensor([1, 1, 1024])]; + tensor var_6865 = transpose(perm = var_6864, x = attn_output_31)[name = tensor("transpose_143")]; + tensor input_153 = reshape(shape = var_6867, x = var_6865)[name = tensor("input_153")]; + tensor attn_out_31 = linear(bias = linear_1_bias_0, weight = attn15_out_proj_weight, x = input_153)[name = tensor("linear_61")]; + tensor var_6873 = const()[name = tensor("op_6873"), val = tensor(0x1p+0)]; + tensor var_6874 = add(x = position15, y = var_6873)[name = tensor("op_6874")]; + tensor input_155 = add(x = input_151, y = attn_out_31)[name = tensor("input_155")]; + tensor var_6878 = const()[name = tensor("op_6878"), val = tensor(0x1.4f8b58p-17)]; + tensor input_157_axes_0 = const()[name = tensor("input_157_axes_0"), val = tensor([-1])]; + tensor input_157 = layer_norm(axes = input_157_axes_0, beta = norm15_2_bias, epsilon = var_6878, gamma = norm15_2_weight, x = input_155)[name = tensor("input_157")]; + tensor var_6886 = linear(bias = linear_2_bias_0, weight = linear15_1_weight, x = input_157)[name = tensor("linear_62")]; + tensor input_159_mode_0 = const()[name = tensor("input_159_mode_0"), val = tensor("EXACT")]; + tensor input_159 = gelu(mode = input_159_mode_0, x = var_6886)[name = tensor("input_159")]; + tensor ffn_out_31 = linear(bias = linear_1_bias_0, weight = linear15_2_weight, x = input_159)[name = tensor("linear_63")]; + tensor input_161 = add(x = input_155, y = ffn_out_31)[name = tensor("input_161")]; + tensor var_6895 = const()[name = tensor("op_6895"), val = tensor(0x1.4f8b58p-17)]; + tensor x_33_axes_0 = const()[name = tensor("x_33_axes_0"), val = tensor([-1])]; + tensor x_33 = layer_norm(axes = x_33_axes_0, beta = norm16_1_bias, epsilon = var_6895, gamma = norm16_1_weight, x = input_161)[name = tensor("x_33")]; + tensor var_6927 = linear(bias = linear_0_bias_0, weight = attn16_in_proj_weight, x = x_33)[name = tensor("linear_64")]; + tensor var_6931 = const()[name = tensor("op_6931"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_33 = reshape(shape = var_6931, x = var_6927)[name = tensor("qkv_33")]; + tensor q_97_begin_0 = const()[name = tensor("q_97_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_97_end_0 = const()[name = tensor("q_97_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_97_end_mask_0 = const()[name = tensor("q_97_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_97_squeeze_mask_0 = const()[name = tensor("q_97_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_97 = slice_by_index(begin = q_97_begin_0, end = q_97_end_0, end_mask = q_97_end_mask_0, squeeze_mask = q_97_squeeze_mask_0, x = qkv_33)[name = tensor("q_97")]; + tensor k_65_begin_0 = const()[name = tensor("k_65_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_65_end_0 = const()[name = tensor("k_65_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_65_end_mask_0 = const()[name = tensor("k_65_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_65_squeeze_mask_0 = const()[name = tensor("k_65_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_65 = slice_by_index(begin = k_65_begin_0, end = k_65_end_0, end_mask = k_65_end_mask_0, squeeze_mask = k_65_squeeze_mask_0, x = qkv_33)[name = tensor("k_65")]; + tensor v_33_begin_0 = const()[name = tensor("v_33_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_33_end_0 = const()[name = tensor("v_33_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_33_end_mask_0 = const()[name = tensor("v_33_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_33_squeeze_mask_0 = const()[name = tensor("v_33_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_33 = slice_by_index(begin = v_33_begin_0, end = v_33_end_0, end_mask = v_33_end_mask_0, squeeze_mask = v_33_squeeze_mask_0, x = qkv_33)[name = tensor("v_33")]; + tensor freqs_33 = const()[name = tensor("freqs_33"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172743232)))]; + tensor var_7035 = const()[name = tensor("op_7035"), val = tensor([1, 1, 1, 1])]; + tensor ts_101 = reshape(shape = var_7035, x = position16)[name = tensor("ts_101")]; + tensor var_7039 = const()[name = tensor("op_7039"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_33 = reshape(shape = var_7039, x = q_97)[name = tensor("q_complex_33")]; + tensor var_7043 = const()[name = tensor("op_7043"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_33 = reshape(shape = var_7043, x = k_65)[name = tensor("k_complex_33")]; + tensor var_7047_begin_0 = const()[name = tensor("op_7047_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7047_end_0 = const()[name = tensor("op_7047_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7047_end_mask_0 = const()[name = tensor("op_7047_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7047_squeeze_mask_0 = const()[name = tensor("op_7047_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7047 = slice_by_index(begin = var_7047_begin_0, end = var_7047_end_0, end_mask = var_7047_end_mask_0, squeeze_mask = var_7047_squeeze_mask_0, x = q_complex_33)[name = tensor("op_7047")]; + tensor var_7055_begin_0 = const()[name = tensor("op_7055_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7055_end_0 = const()[name = tensor("op_7055_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7055_end_mask_0 = const()[name = tensor("op_7055_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7055_squeeze_mask_0 = const()[name = tensor("op_7055_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7055 = slice_by_index(begin = var_7055_begin_0, end = var_7055_end_0, end_mask = var_7055_end_mask_0, squeeze_mask = var_7055_squeeze_mask_0, x = q_complex_33)[name = tensor("op_7055")]; + tensor var_7063_begin_0 = const()[name = tensor("op_7063_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7063_end_0 = const()[name = tensor("op_7063_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7063_end_mask_0 = const()[name = tensor("op_7063_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7063_squeeze_mask_0 = const()[name = tensor("op_7063_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7063 = slice_by_index(begin = var_7063_begin_0, end = var_7063_end_0, end_mask = var_7063_end_mask_0, squeeze_mask = var_7063_squeeze_mask_0, x = k_complex_33)[name = tensor("op_7063")]; + tensor var_7071_begin_0 = const()[name = tensor("op_7071_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7071_end_0 = const()[name = tensor("op_7071_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7071_end_mask_0 = const()[name = tensor("op_7071_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7071_squeeze_mask_0 = const()[name = tensor("op_7071_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7071 = slice_by_index(begin = var_7071_begin_0, end = var_7071_end_0, end_mask = var_7071_end_mask_0, squeeze_mask = var_7071_squeeze_mask_0, x = k_complex_33)[name = tensor("op_7071")]; + tensor var_7077 = mul(x = freqs_33, y = ts_101)[name = tensor("op_7077")]; + tensor rotr_33 = cos(x = var_7077)[name = tensor("rotr_33")]; + tensor roti_33 = sin(x = var_7077)[name = tensor("roti_33")]; + tensor var_7081 = mul(x = var_7047, y = rotr_33)[name = tensor("op_7081")]; + tensor var_7082 = mul(x = var_7055, y = roti_33)[name = tensor("op_7082")]; + tensor qor_65 = sub(x = var_7081, y = var_7082)[name = tensor("qor_65")]; + tensor var_7085 = mul(x = var_7047, y = roti_33)[name = tensor("op_7085")]; + tensor var_7086 = mul(x = var_7055, y = rotr_33)[name = tensor("op_7086")]; + tensor qoi_65 = add(x = var_7085, y = var_7086)[name = tensor("qoi_65")]; + tensor var_7089 = mul(x = var_7063, y = rotr_33)[name = tensor("op_7089")]; + tensor var_7090 = mul(x = var_7071, y = roti_33)[name = tensor("op_7090")]; + tensor kor_65 = sub(x = var_7089, y = var_7090)[name = tensor("kor_65")]; + tensor var_7093 = mul(x = var_7063, y = roti_33)[name = tensor("op_7093")]; + tensor var_7094 = mul(x = var_7071, y = rotr_33)[name = tensor("op_7094")]; + tensor koi_65 = add(x = var_7093, y = var_7094)[name = tensor("koi_65")]; + tensor qo_33_axis_0 = const()[name = tensor("qo_33_axis_0"), val = tensor(-1)]; + tensor qo_33 = stack(axis = qo_33_axis_0, values = (qor_65, qoi_65))[name = tensor("qo_33")]; + tensor ko_33_axis_0 = const()[name = tensor("ko_33_axis_0"), val = tensor(-1)]; + tensor ko_33 = stack(axis = ko_33_axis_0, values = (kor_65, koi_65))[name = tensor("ko_33")]; + tensor var_7123 = const()[name = tensor("op_7123"), val = tensor([1, 1, 16, 64])]; + tensor q_99 = reshape(shape = var_7123, x = qo_33)[name = tensor("q_99")]; + tensor var_7125 = const()[name = tensor("op_7125"), val = tensor([1, 1, 16, 64])]; + tensor k_67 = reshape(shape = var_7125, x = ko_33)[name = tensor("k_67")]; + tensor _inversed_7147_y_0 = const()[name = tensor("_inversed_7147_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_7147 = mul(x = ts_101, y = _inversed_7147_y_0)[name = tensor("_inversed_7147")]; + tensor var_7148 = floor(x = _inversed_7147)[name = tensor("op_7148")]; + tensor var_7149 = const()[name = tensor("op_7149"), val = tensor(0x1p+9)]; + tensor var_7150 = mul(x = var_7148, y = var_7149)[name = tensor("op_7150")]; + tensor write_indices_float_67 = sub(x = ts_101, y = var_7150)[name = tensor("write_indices_float_67")]; + tensor var_7157_dtype_0 = const()[name = tensor("op_7157_dtype_0"), val = tensor("int32")]; + tensor write_indices_33_reps_0 = const()[name = tensor("write_indices_33_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_7157 = cast(dtype = var_7157_dtype_0, x = write_indices_float_67)[name = tensor("cast_430")]; + tensor write_indices_33 = tile(reps = write_indices_33_reps_0, x = var_7157)[name = tensor("write_indices_33")]; + tensor var_7165_begin_0 = const()[name = tensor("op_7165_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7165_end_0 = const()[name = tensor("op_7165_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_7165_end_mask_0 = const()[name = tensor("op_7165_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7165_squeeze_mask_0 = const()[name = tensor("op_7165_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7165 = slice_by_index(begin = var_7165_begin_0, end = var_7165_end_0, end_mask = var_7165_end_mask_0, squeeze_mask = var_7165_squeeze_mask_0, x = cache16)[name = tensor("op_7165")]; + tensor var_7167_axis_0 = const()[name = tensor("op_7167_axis_0"), val = tensor(1)]; + tensor var_7167_mode_0 = const()[name = tensor("op_7167_mode_0"), val = tensor("update")]; + tensor var_7167_validate_indices_0 = const()[name = tensor("op_7167_validate_indices_0"), val = tensor(false)]; + tensor var_7167 = scatter_along_axis(axis = var_7167_axis_0, data = var_7165, indices = write_indices_33, mode = var_7167_mode_0, updates = k_67, validate_indices = var_7167_validate_indices_0)[name = tensor("op_7167")]; + tensor concat_113 = const()[name = tensor("concat_113"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_114 = const()[name = tensor("concat_114"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_33_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_33_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_78 = const()[name = tensor("shape_78"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_32 = const()[name = tensor("reduce_prod_32"), val = tensor(1048576)]; + tensor range_1d_32_start_0 = const()[name = tensor("range_1d_32_start_0"), val = tensor(0)]; + tensor range_1d_32_step_0 = const()[name = tensor("range_1d_32_step_0"), val = tensor(1)]; + tensor range_1d_32 = range_1d(end = reduce_prod_32, start = range_1d_32_start_0, step = range_1d_32_step_0)[name = tensor("range_1d_32")]; + tensor reshape_160 = reshape(shape = shape_78, x = range_1d_32)[name = tensor("reshape_160")]; + tensor slice_by_index_32 = slice_by_index(begin = concat_113, begin_mask = new_cache_33_internal_tensor_assign_1_begin_mask_0, end = concat_114, end_mask = new_cache_33_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_33_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_33_internal_tensor_assign_1_stride_0, x = reshape_160)[name = tensor("slice_by_index_32")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([-1])]; + tensor reshape_161 = reshape(shape = reshape_161_shape_0, x = slice_by_index_32)[name = tensor("reshape_161")]; + tensor reshape_162_shape_0 = const()[name = tensor("reshape_162_shape_0"), val = tensor([-1])]; + tensor reshape_162 = reshape(shape = reshape_162_shape_0, x = var_7167)[name = tensor("reshape_162")]; + tensor reshape_163_shape_0 = const()[name = tensor("reshape_163_shape_0"), val = tensor([-1])]; + tensor reshape_163 = reshape(shape = reshape_163_shape_0, x = cache16)[name = tensor("reshape_163")]; + tensor scatter_32_mode_0 = const()[name = tensor("scatter_32_mode_0"), val = tensor("update")]; + tensor scatter_32_axis_0 = const()[name = tensor("scatter_32_axis_0"), val = tensor(0)]; + tensor scatter_32_validate_indices_0 = const()[name = tensor("scatter_32_validate_indices_0"), val = tensor(false)]; + tensor scatter_32 = scatter(axis = scatter_32_axis_0, data = reshape_163, indices = reshape_161, mode = scatter_32_mode_0, updates = reshape_162, validate_indices = scatter_32_validate_indices_0)[name = tensor("scatter_32")]; + tensor reshape_164 = reshape(shape = shape_78, x = scatter_32)[name = tensor("reshape_164")]; + tensor var_7175_begin_0 = const()[name = tensor("op_7175_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_7175_end_0 = const()[name = tensor("op_7175_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_7175_end_mask_0 = const()[name = tensor("op_7175_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7175_squeeze_mask_0 = const()[name = tensor("op_7175_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7175 = slice_by_index(begin = var_7175_begin_0, end = var_7175_end_0, end_mask = var_7175_end_mask_0, squeeze_mask = var_7175_squeeze_mask_0, x = reshape_164)[name = tensor("op_7175")]; + tensor var_7177_axis_0 = const()[name = tensor("op_7177_axis_0"), val = tensor(1)]; + tensor var_7177_mode_0 = const()[name = tensor("op_7177_mode_0"), val = tensor("update")]; + tensor var_7177_validate_indices_0 = const()[name = tensor("op_7177_validate_indices_0"), val = tensor(false)]; + tensor var_7177 = scatter_along_axis(axis = var_7177_axis_0, data = var_7175, indices = write_indices_33, mode = var_7177_mode_0, updates = v_33, validate_indices = var_7177_validate_indices_0)[name = tensor("op_7177")]; + tensor concat_115 = const()[name = tensor("concat_115"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_116 = const()[name = tensor("concat_116"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_33_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_33_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_79 = const()[name = tensor("shape_79"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_33 = const()[name = tensor("reduce_prod_33"), val = tensor(1048576)]; + tensor range_1d_33_start_0 = const()[name = tensor("range_1d_33_start_0"), val = tensor(0)]; + tensor range_1d_33_step_0 = const()[name = tensor("range_1d_33_step_0"), val = tensor(1)]; + tensor range_1d_33 = range_1d(end = reduce_prod_33, start = range_1d_33_start_0, step = range_1d_33_step_0)[name = tensor("range_1d_33")]; + tensor reshape_165 = reshape(shape = shape_79, x = range_1d_33)[name = tensor("reshape_165")]; + tensor slice_by_index_33 = slice_by_index(begin = concat_115, begin_mask = new_cache_33_internal_tensor_assign_2_begin_mask_0, end = concat_116, end_mask = new_cache_33_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_33_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_33_internal_tensor_assign_2_stride_0, x = reshape_165)[name = tensor("slice_by_index_33")]; + tensor reshape_166_shape_0 = const()[name = tensor("reshape_166_shape_0"), val = tensor([-1])]; + tensor reshape_166 = reshape(shape = reshape_166_shape_0, x = slice_by_index_33)[name = tensor("reshape_166")]; + tensor reshape_167_shape_0 = const()[name = tensor("reshape_167_shape_0"), val = tensor([-1])]; + tensor reshape_167 = reshape(shape = reshape_167_shape_0, x = var_7177)[name = tensor("reshape_167")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([-1])]; + tensor reshape_168 = reshape(shape = reshape_168_shape_0, x = reshape_164)[name = tensor("reshape_168")]; + tensor scatter_33_mode_0 = const()[name = tensor("scatter_33_mode_0"), val = tensor("update")]; + tensor scatter_33_axis_0 = const()[name = tensor("scatter_33_axis_0"), val = tensor(0)]; + tensor scatter_33_validate_indices_0 = const()[name = tensor("scatter_33_validate_indices_0"), val = tensor(false)]; + tensor scatter_33 = scatter(axis = scatter_33_axis_0, data = reshape_168, indices = reshape_166, mode = scatter_33_mode_0, updates = reshape_167, validate_indices = scatter_33_validate_indices_0)[name = tensor("scatter_33")]; + tensor new_cache_33_internal_tensor_assign_2 = reshape(shape = shape_79, x = scatter_33)[name = tensor("reshape_169")]; + tensor keys_97_begin_0 = const()[name = tensor("keys_97_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_97_end_0 = const()[name = tensor("keys_97_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_97_end_mask_0 = const()[name = tensor("keys_97_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_97_squeeze_mask_0 = const()[name = tensor("keys_97_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_97 = slice_by_index(begin = keys_97_begin_0, end = keys_97_end_0, end_mask = keys_97_end_mask_0, squeeze_mask = keys_97_squeeze_mask_0, x = new_cache_33_internal_tensor_assign_2)[name = tensor("keys_97")]; + tensor values_97_begin_0 = const()[name = tensor("values_97_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_97_end_0 = const()[name = tensor("values_97_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_97_end_mask_0 = const()[name = tensor("values_97_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_97_squeeze_mask_0 = const()[name = tensor("values_97_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_97 = slice_by_index(begin = values_97_begin_0, end = values_97_end_0, end_mask = values_97_end_mask_0, squeeze_mask = values_97_squeeze_mask_0, x = new_cache_33_internal_tensor_assign_2)[name = tensor("values_97")]; + tensor var_7189 = not_equal(x = keys_97, y = keys_97)[name = tensor("op_7189")]; + tensor keys_99 = select(a = var_491, b = keys_97, cond = var_7189)[name = tensor("keys_99")]; + tensor var_7197 = not_equal(x = values_97, y = values_97)[name = tensor("op_7197")]; + tensor values_99 = select(a = var_491, b = values_97, cond = var_7197)[name = tensor("values_99")]; + tensor var_7221 = const()[name = tensor("op_7221"), val = tensor([0, 2, 1, 3])]; + tensor var_7234 = const()[name = tensor("op_7234"), val = tensor([1, 1, 1])]; + tensor var_7235 = reshape(shape = var_7234, x = position16)[name = tensor("op_7235")]; + tensor var_7252 = const()[name = tensor("op_7252"), val = tensor(0x1p+0)]; + tensor valid_len_33 = add(x = var_7235, y = var_7252)[name = tensor("valid_len_33")]; + tensor valid_mask_33 = less(x = k_positions_1_promoted, y = valid_len_33)[name = tensor("valid_mask_33")]; + tensor causal_mask_33 = less_equal(x = k_positions_1_promoted, y = var_7235)[name = tensor("causal_mask_33")]; + tensor attn_mask_65 = logical_and(x = valid_mask_33, y = causal_mask_33)[name = tensor("attn_mask_65")]; + tensor attn_mask_67_axes_0 = const()[name = tensor("attn_mask_67_axes_0"), val = tensor([1])]; + tensor attn_mask_67 = expand_dims(axes = attn_mask_67_axes_0, x = attn_mask_65)[name = tensor("attn_mask_67")]; + tensor var_7264 = const()[name = tensor("op_7264"), val = tensor([0x1.fffe5cp-4])]; + tensor var_7270_transpose_x_0 = const()[name = tensor("op_7270_transpose_x_0"), val = tensor(false)]; + tensor var_7270_transpose_y_0 = const()[name = tensor("op_7270_transpose_y_0"), val = tensor(false)]; + tensor transpose_101_perm_0 = const()[name = tensor("transpose_101_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_102_perm_0 = const()[name = tensor("transpose_102_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = keys_99)[name = tensor("transpose_140")]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = q_99)[name = tensor("transpose_141")]; + tensor var_7270 = matmul(transpose_x = var_7270_transpose_x_0, transpose_y = var_7270_transpose_y_0, x = transpose_101, y = transpose_102)[name = tensor("op_7270")]; + tensor attn_weights_97 = mul(x = var_7270, y = var_7264)[name = tensor("attn_weights_97")]; + tensor var_7272 = logical_not(x = attn_mask_67)[name = tensor("op_7272")]; + tensor var_7273 = const()[name = tensor("op_7273"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_99 = select(a = var_7273, b = attn_weights_97, cond = var_7272)[name = tensor("attn_weights_99")]; + tensor var_7275 = const()[name = tensor("op_7275"), val = tensor(-1)]; + tensor attn_weights_101 = softmax(axis = var_7275, x = attn_weights_99)[name = tensor("attn_weights_101")]; + tensor attn_output_33_transpose_x_0 = const()[name = tensor("attn_output_33_transpose_x_0"), val = tensor(false)]; + tensor attn_output_33_transpose_y_0 = const()[name = tensor("attn_output_33_transpose_y_0"), val = tensor(false)]; + tensor values_101 = transpose(perm = var_7221, x = values_99)[name = tensor("transpose_142")]; + tensor attn_output_33 = matmul(transpose_x = attn_output_33_transpose_x_0, transpose_y = attn_output_33_transpose_y_0, x = attn_weights_101, y = values_101)[name = tensor("attn_output_33")]; + tensor var_7283 = const()[name = tensor("op_7283"), val = tensor([0, 2, 1, 3])]; + tensor var_7286 = const()[name = tensor("op_7286"), val = tensor([1, 1, 1024])]; + tensor var_7284 = transpose(perm = var_7283, x = attn_output_33)[name = tensor("transpose_139")]; + tensor input_163 = reshape(shape = var_7286, x = var_7284)[name = tensor("input_163")]; + tensor attn_out_33 = linear(bias = linear_1_bias_0, weight = attn16_out_proj_weight, x = input_163)[name = tensor("linear_65")]; + tensor var_7292 = const()[name = tensor("op_7292"), val = tensor(0x1p+0)]; + tensor var_7293 = add(x = position16, y = var_7292)[name = tensor("op_7293")]; + tensor input_165 = add(x = input_161, y = attn_out_33)[name = tensor("input_165")]; + tensor var_7297 = const()[name = tensor("op_7297"), val = tensor(0x1.4f8b58p-17)]; + tensor input_167_axes_0 = const()[name = tensor("input_167_axes_0"), val = tensor([-1])]; + tensor input_167 = layer_norm(axes = input_167_axes_0, beta = norm16_2_bias, epsilon = var_7297, gamma = norm16_2_weight, x = input_165)[name = tensor("input_167")]; + tensor var_7305 = linear(bias = linear_2_bias_0, weight = linear16_1_weight, x = input_167)[name = tensor("linear_66")]; + tensor input_169_mode_0 = const()[name = tensor("input_169_mode_0"), val = tensor("EXACT")]; + tensor input_169 = gelu(mode = input_169_mode_0, x = var_7305)[name = tensor("input_169")]; + tensor ffn_out_33 = linear(bias = linear_1_bias_0, weight = linear16_2_weight, x = input_169)[name = tensor("linear_67")]; + tensor input_171 = add(x = input_165, y = ffn_out_33)[name = tensor("input_171")]; + tensor var_7314 = const()[name = tensor("op_7314"), val = tensor(0x1.4f8b58p-17)]; + tensor x_35_axes_0 = const()[name = tensor("x_35_axes_0"), val = tensor([-1])]; + tensor x_35 = layer_norm(axes = x_35_axes_0, beta = norm17_1_bias, epsilon = var_7314, gamma = norm17_1_weight, x = input_171)[name = tensor("x_35")]; + tensor var_7346 = linear(bias = linear_0_bias_0, weight = attn17_in_proj_weight, x = x_35)[name = tensor("linear_68")]; + tensor var_7350 = const()[name = tensor("op_7350"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_35 = reshape(shape = var_7350, x = var_7346)[name = tensor("qkv_35")]; + tensor q_103_begin_0 = const()[name = tensor("q_103_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_103_end_0 = const()[name = tensor("q_103_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_103_end_mask_0 = const()[name = tensor("q_103_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_103_squeeze_mask_0 = const()[name = tensor("q_103_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_103 = slice_by_index(begin = q_103_begin_0, end = q_103_end_0, end_mask = q_103_end_mask_0, squeeze_mask = q_103_squeeze_mask_0, x = qkv_35)[name = tensor("q_103")]; + tensor k_69_begin_0 = const()[name = tensor("k_69_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_69_end_0 = const()[name = tensor("k_69_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_69_end_mask_0 = const()[name = tensor("k_69_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_69_squeeze_mask_0 = const()[name = tensor("k_69_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_69 = slice_by_index(begin = k_69_begin_0, end = k_69_end_0, end_mask = k_69_end_mask_0, squeeze_mask = k_69_squeeze_mask_0, x = qkv_35)[name = tensor("k_69")]; + tensor v_35_begin_0 = const()[name = tensor("v_35_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_35_end_0 = const()[name = tensor("v_35_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_35_end_mask_0 = const()[name = tensor("v_35_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_35_squeeze_mask_0 = const()[name = tensor("v_35_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_35 = slice_by_index(begin = v_35_begin_0, end = v_35_end_0, end_mask = v_35_end_mask_0, squeeze_mask = v_35_squeeze_mask_0, x = qkv_35)[name = tensor("v_35")]; + tensor freqs_35 = const()[name = tensor("freqs_35"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172743424)))]; + tensor var_7454 = const()[name = tensor("op_7454"), val = tensor([1, 1, 1, 1])]; + tensor ts_107 = reshape(shape = var_7454, x = position17)[name = tensor("ts_107")]; + tensor var_7458 = const()[name = tensor("op_7458"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_35 = reshape(shape = var_7458, x = q_103)[name = tensor("q_complex_35")]; + tensor var_7462 = const()[name = tensor("op_7462"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_35 = reshape(shape = var_7462, x = k_69)[name = tensor("k_complex_35")]; + tensor var_7466_begin_0 = const()[name = tensor("op_7466_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7466_end_0 = const()[name = tensor("op_7466_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7466_end_mask_0 = const()[name = tensor("op_7466_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7466_squeeze_mask_0 = const()[name = tensor("op_7466_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7466 = slice_by_index(begin = var_7466_begin_0, end = var_7466_end_0, end_mask = var_7466_end_mask_0, squeeze_mask = var_7466_squeeze_mask_0, x = q_complex_35)[name = tensor("op_7466")]; + tensor var_7474_begin_0 = const()[name = tensor("op_7474_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7474_end_0 = const()[name = tensor("op_7474_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7474_end_mask_0 = const()[name = tensor("op_7474_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7474_squeeze_mask_0 = const()[name = tensor("op_7474_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7474 = slice_by_index(begin = var_7474_begin_0, end = var_7474_end_0, end_mask = var_7474_end_mask_0, squeeze_mask = var_7474_squeeze_mask_0, x = q_complex_35)[name = tensor("op_7474")]; + tensor var_7482_begin_0 = const()[name = tensor("op_7482_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7482_end_0 = const()[name = tensor("op_7482_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7482_end_mask_0 = const()[name = tensor("op_7482_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7482_squeeze_mask_0 = const()[name = tensor("op_7482_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7482 = slice_by_index(begin = var_7482_begin_0, end = var_7482_end_0, end_mask = var_7482_end_mask_0, squeeze_mask = var_7482_squeeze_mask_0, x = k_complex_35)[name = tensor("op_7482")]; + tensor var_7490_begin_0 = const()[name = tensor("op_7490_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7490_end_0 = const()[name = tensor("op_7490_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7490_end_mask_0 = const()[name = tensor("op_7490_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7490_squeeze_mask_0 = const()[name = tensor("op_7490_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7490 = slice_by_index(begin = var_7490_begin_0, end = var_7490_end_0, end_mask = var_7490_end_mask_0, squeeze_mask = var_7490_squeeze_mask_0, x = k_complex_35)[name = tensor("op_7490")]; + tensor var_7496 = mul(x = freqs_35, y = ts_107)[name = tensor("op_7496")]; + tensor rotr_35 = cos(x = var_7496)[name = tensor("rotr_35")]; + tensor roti_35 = sin(x = var_7496)[name = tensor("roti_35")]; + tensor var_7500 = mul(x = var_7466, y = rotr_35)[name = tensor("op_7500")]; + tensor var_7501 = mul(x = var_7474, y = roti_35)[name = tensor("op_7501")]; + tensor qor_69 = sub(x = var_7500, y = var_7501)[name = tensor("qor_69")]; + tensor var_7504 = mul(x = var_7466, y = roti_35)[name = tensor("op_7504")]; + tensor var_7505 = mul(x = var_7474, y = rotr_35)[name = tensor("op_7505")]; + tensor qoi_69 = add(x = var_7504, y = var_7505)[name = tensor("qoi_69")]; + tensor var_7508 = mul(x = var_7482, y = rotr_35)[name = tensor("op_7508")]; + tensor var_7509 = mul(x = var_7490, y = roti_35)[name = tensor("op_7509")]; + tensor kor_69 = sub(x = var_7508, y = var_7509)[name = tensor("kor_69")]; + tensor var_7512 = mul(x = var_7482, y = roti_35)[name = tensor("op_7512")]; + tensor var_7513 = mul(x = var_7490, y = rotr_35)[name = tensor("op_7513")]; + tensor koi_69 = add(x = var_7512, y = var_7513)[name = tensor("koi_69")]; + tensor qo_35_axis_0 = const()[name = tensor("qo_35_axis_0"), val = tensor(-1)]; + tensor qo_35 = stack(axis = qo_35_axis_0, values = (qor_69, qoi_69))[name = tensor("qo_35")]; + tensor ko_35_axis_0 = const()[name = tensor("ko_35_axis_0"), val = tensor(-1)]; + tensor ko_35 = stack(axis = ko_35_axis_0, values = (kor_69, koi_69))[name = tensor("ko_35")]; + tensor var_7542 = const()[name = tensor("op_7542"), val = tensor([1, 1, 16, 64])]; + tensor q_105 = reshape(shape = var_7542, x = qo_35)[name = tensor("q_105")]; + tensor var_7544 = const()[name = tensor("op_7544"), val = tensor([1, 1, 16, 64])]; + tensor k_71 = reshape(shape = var_7544, x = ko_35)[name = tensor("k_71")]; + tensor _inversed_7566_y_0 = const()[name = tensor("_inversed_7566_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_7566 = mul(x = ts_107, y = _inversed_7566_y_0)[name = tensor("_inversed_7566")]; + tensor var_7567 = floor(x = _inversed_7566)[name = tensor("op_7567")]; + tensor var_7568 = const()[name = tensor("op_7568"), val = tensor(0x1p+9)]; + tensor var_7569 = mul(x = var_7567, y = var_7568)[name = tensor("op_7569")]; + tensor write_indices_float_71 = sub(x = ts_107, y = var_7569)[name = tensor("write_indices_float_71")]; + tensor var_7576_dtype_0 = const()[name = tensor("op_7576_dtype_0"), val = tensor("int32")]; + tensor write_indices_35_reps_0 = const()[name = tensor("write_indices_35_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_7576 = cast(dtype = var_7576_dtype_0, x = write_indices_float_71)[name = tensor("cast_429")]; + tensor write_indices_35 = tile(reps = write_indices_35_reps_0, x = var_7576)[name = tensor("write_indices_35")]; + tensor var_7584_begin_0 = const()[name = tensor("op_7584_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7584_end_0 = const()[name = tensor("op_7584_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_7584_end_mask_0 = const()[name = tensor("op_7584_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7584_squeeze_mask_0 = const()[name = tensor("op_7584_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7584 = slice_by_index(begin = var_7584_begin_0, end = var_7584_end_0, end_mask = var_7584_end_mask_0, squeeze_mask = var_7584_squeeze_mask_0, x = cache17)[name = tensor("op_7584")]; + tensor var_7586_axis_0 = const()[name = tensor("op_7586_axis_0"), val = tensor(1)]; + tensor var_7586_mode_0 = const()[name = tensor("op_7586_mode_0"), val = tensor("update")]; + tensor var_7586_validate_indices_0 = const()[name = tensor("op_7586_validate_indices_0"), val = tensor(false)]; + tensor var_7586 = scatter_along_axis(axis = var_7586_axis_0, data = var_7584, indices = write_indices_35, mode = var_7586_mode_0, updates = k_71, validate_indices = var_7586_validate_indices_0)[name = tensor("op_7586")]; + tensor concat_120 = const()[name = tensor("concat_120"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_121 = const()[name = tensor("concat_121"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_35_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_35_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_80 = const()[name = tensor("shape_80"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_34 = const()[name = tensor("reduce_prod_34"), val = tensor(1048576)]; + tensor range_1d_34_start_0 = const()[name = tensor("range_1d_34_start_0"), val = tensor(0)]; + tensor range_1d_34_step_0 = const()[name = tensor("range_1d_34_step_0"), val = tensor(1)]; + tensor range_1d_34 = range_1d(end = reduce_prod_34, start = range_1d_34_start_0, step = range_1d_34_step_0)[name = tensor("range_1d_34")]; + tensor reshape_170 = reshape(shape = shape_80, x = range_1d_34)[name = tensor("reshape_170")]; + tensor slice_by_index_34 = slice_by_index(begin = concat_120, begin_mask = new_cache_35_internal_tensor_assign_1_begin_mask_0, end = concat_121, end_mask = new_cache_35_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_35_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_35_internal_tensor_assign_1_stride_0, x = reshape_170)[name = tensor("slice_by_index_34")]; + tensor reshape_171_shape_0 = const()[name = tensor("reshape_171_shape_0"), val = tensor([-1])]; + tensor reshape_171 = reshape(shape = reshape_171_shape_0, x = slice_by_index_34)[name = tensor("reshape_171")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([-1])]; + tensor reshape_172 = reshape(shape = reshape_172_shape_0, x = var_7586)[name = tensor("reshape_172")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([-1])]; + tensor reshape_173 = reshape(shape = reshape_173_shape_0, x = cache17)[name = tensor("reshape_173")]; + tensor scatter_34_mode_0 = const()[name = tensor("scatter_34_mode_0"), val = tensor("update")]; + tensor scatter_34_axis_0 = const()[name = tensor("scatter_34_axis_0"), val = tensor(0)]; + tensor scatter_34_validate_indices_0 = const()[name = tensor("scatter_34_validate_indices_0"), val = tensor(false)]; + tensor scatter_34 = scatter(axis = scatter_34_axis_0, data = reshape_173, indices = reshape_171, mode = scatter_34_mode_0, updates = reshape_172, validate_indices = scatter_34_validate_indices_0)[name = tensor("scatter_34")]; + tensor reshape_174 = reshape(shape = shape_80, x = scatter_34)[name = tensor("reshape_174")]; + tensor var_7594_begin_0 = const()[name = tensor("op_7594_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_7594_end_0 = const()[name = tensor("op_7594_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_7594_end_mask_0 = const()[name = tensor("op_7594_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7594_squeeze_mask_0 = const()[name = tensor("op_7594_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7594 = slice_by_index(begin = var_7594_begin_0, end = var_7594_end_0, end_mask = var_7594_end_mask_0, squeeze_mask = var_7594_squeeze_mask_0, x = reshape_174)[name = tensor("op_7594")]; + tensor var_7596_axis_0 = const()[name = tensor("op_7596_axis_0"), val = tensor(1)]; + tensor var_7596_mode_0 = const()[name = tensor("op_7596_mode_0"), val = tensor("update")]; + tensor var_7596_validate_indices_0 = const()[name = tensor("op_7596_validate_indices_0"), val = tensor(false)]; + tensor var_7596 = scatter_along_axis(axis = var_7596_axis_0, data = var_7594, indices = write_indices_35, mode = var_7596_mode_0, updates = v_35, validate_indices = var_7596_validate_indices_0)[name = tensor("op_7596")]; + tensor concat_122 = const()[name = tensor("concat_122"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_123 = const()[name = tensor("concat_123"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_35_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_35_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_81 = const()[name = tensor("shape_81"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_35 = const()[name = tensor("reduce_prod_35"), val = tensor(1048576)]; + tensor range_1d_35_start_0 = const()[name = tensor("range_1d_35_start_0"), val = tensor(0)]; + tensor range_1d_35_step_0 = const()[name = tensor("range_1d_35_step_0"), val = tensor(1)]; + tensor range_1d_35 = range_1d(end = reduce_prod_35, start = range_1d_35_start_0, step = range_1d_35_step_0)[name = tensor("range_1d_35")]; + tensor reshape_175 = reshape(shape = shape_81, x = range_1d_35)[name = tensor("reshape_175")]; + tensor slice_by_index_35 = slice_by_index(begin = concat_122, begin_mask = new_cache_35_internal_tensor_assign_2_begin_mask_0, end = concat_123, end_mask = new_cache_35_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_35_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_35_internal_tensor_assign_2_stride_0, x = reshape_175)[name = tensor("slice_by_index_35")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([-1])]; + tensor reshape_176 = reshape(shape = reshape_176_shape_0, x = slice_by_index_35)[name = tensor("reshape_176")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([-1])]; + tensor reshape_177 = reshape(shape = reshape_177_shape_0, x = var_7596)[name = tensor("reshape_177")]; + tensor reshape_178_shape_0 = const()[name = tensor("reshape_178_shape_0"), val = tensor([-1])]; + tensor reshape_178 = reshape(shape = reshape_178_shape_0, x = reshape_174)[name = tensor("reshape_178")]; + tensor scatter_35_mode_0 = const()[name = tensor("scatter_35_mode_0"), val = tensor("update")]; + tensor scatter_35_axis_0 = const()[name = tensor("scatter_35_axis_0"), val = tensor(0)]; + tensor scatter_35_validate_indices_0 = const()[name = tensor("scatter_35_validate_indices_0"), val = tensor(false)]; + tensor scatter_35 = scatter(axis = scatter_35_axis_0, data = reshape_178, indices = reshape_176, mode = scatter_35_mode_0, updates = reshape_177, validate_indices = scatter_35_validate_indices_0)[name = tensor("scatter_35")]; + tensor new_cache_35_internal_tensor_assign_2 = reshape(shape = shape_81, x = scatter_35)[name = tensor("reshape_179")]; + tensor keys_103_begin_0 = const()[name = tensor("keys_103_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_103_end_0 = const()[name = tensor("keys_103_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_103_end_mask_0 = const()[name = tensor("keys_103_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_103_squeeze_mask_0 = const()[name = tensor("keys_103_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_103 = slice_by_index(begin = keys_103_begin_0, end = keys_103_end_0, end_mask = keys_103_end_mask_0, squeeze_mask = keys_103_squeeze_mask_0, x = new_cache_35_internal_tensor_assign_2)[name = tensor("keys_103")]; + tensor values_103_begin_0 = const()[name = tensor("values_103_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_103_end_0 = const()[name = tensor("values_103_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_103_end_mask_0 = const()[name = tensor("values_103_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_103_squeeze_mask_0 = const()[name = tensor("values_103_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_103 = slice_by_index(begin = values_103_begin_0, end = values_103_end_0, end_mask = values_103_end_mask_0, squeeze_mask = values_103_squeeze_mask_0, x = new_cache_35_internal_tensor_assign_2)[name = tensor("values_103")]; + tensor var_7608 = not_equal(x = keys_103, y = keys_103)[name = tensor("op_7608")]; + tensor keys_105 = select(a = var_491, b = keys_103, cond = var_7608)[name = tensor("keys_105")]; + tensor var_7616 = not_equal(x = values_103, y = values_103)[name = tensor("op_7616")]; + tensor values_105 = select(a = var_491, b = values_103, cond = var_7616)[name = tensor("values_105")]; + tensor var_7640 = const()[name = tensor("op_7640"), val = tensor([0, 2, 1, 3])]; + tensor var_7653 = const()[name = tensor("op_7653"), val = tensor([1, 1, 1])]; + tensor var_7654 = reshape(shape = var_7653, x = position17)[name = tensor("op_7654")]; + tensor var_7671 = const()[name = tensor("op_7671"), val = tensor(0x1p+0)]; + tensor valid_len_35 = add(x = var_7654, y = var_7671)[name = tensor("valid_len_35")]; + tensor valid_mask_35 = less(x = k_positions_1_promoted, y = valid_len_35)[name = tensor("valid_mask_35")]; + tensor causal_mask_35 = less_equal(x = k_positions_1_promoted, y = var_7654)[name = tensor("causal_mask_35")]; + tensor attn_mask_69 = logical_and(x = valid_mask_35, y = causal_mask_35)[name = tensor("attn_mask_69")]; + tensor attn_mask_71_axes_0 = const()[name = tensor("attn_mask_71_axes_0"), val = tensor([1])]; + tensor attn_mask_71 = expand_dims(axes = attn_mask_71_axes_0, x = attn_mask_69)[name = tensor("attn_mask_71")]; + tensor var_7683 = const()[name = tensor("op_7683"), val = tensor([0x1.fffe5cp-4])]; + tensor var_7689_transpose_x_0 = const()[name = tensor("op_7689_transpose_x_0"), val = tensor(false)]; + tensor var_7689_transpose_y_0 = const()[name = tensor("op_7689_transpose_y_0"), val = tensor(false)]; + tensor transpose_103_perm_0 = const()[name = tensor("transpose_103_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_104_perm_0 = const()[name = tensor("transpose_104_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = keys_105)[name = tensor("transpose_136")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = q_105)[name = tensor("transpose_137")]; + tensor var_7689 = matmul(transpose_x = var_7689_transpose_x_0, transpose_y = var_7689_transpose_y_0, x = transpose_103, y = transpose_104)[name = tensor("op_7689")]; + tensor attn_weights_103 = mul(x = var_7689, y = var_7683)[name = tensor("attn_weights_103")]; + tensor var_7691 = logical_not(x = attn_mask_71)[name = tensor("op_7691")]; + tensor var_7692 = const()[name = tensor("op_7692"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_105 = select(a = var_7692, b = attn_weights_103, cond = var_7691)[name = tensor("attn_weights_105")]; + tensor var_7694 = const()[name = tensor("op_7694"), val = tensor(-1)]; + tensor attn_weights_107 = softmax(axis = var_7694, x = attn_weights_105)[name = tensor("attn_weights_107")]; + tensor attn_output_35_transpose_x_0 = const()[name = tensor("attn_output_35_transpose_x_0"), val = tensor(false)]; + tensor attn_output_35_transpose_y_0 = const()[name = tensor("attn_output_35_transpose_y_0"), val = tensor(false)]; + tensor values_107 = transpose(perm = var_7640, x = values_105)[name = tensor("transpose_138")]; + tensor attn_output_35 = matmul(transpose_x = attn_output_35_transpose_x_0, transpose_y = attn_output_35_transpose_y_0, x = attn_weights_107, y = values_107)[name = tensor("attn_output_35")]; + tensor var_7702 = const()[name = tensor("op_7702"), val = tensor([0, 2, 1, 3])]; + tensor var_7705 = const()[name = tensor("op_7705"), val = tensor([1, 1, 1024])]; + tensor var_7703 = transpose(perm = var_7702, x = attn_output_35)[name = tensor("transpose_135")]; + tensor input_173 = reshape(shape = var_7705, x = var_7703)[name = tensor("input_173")]; + tensor attn_out_35 = linear(bias = linear_1_bias_0, weight = attn17_out_proj_weight, x = input_173)[name = tensor("linear_69")]; + tensor var_7711 = const()[name = tensor("op_7711"), val = tensor(0x1p+0)]; + tensor var_7712 = add(x = position17, y = var_7711)[name = tensor("op_7712")]; + tensor input_175 = add(x = input_171, y = attn_out_35)[name = tensor("input_175")]; + tensor var_7716 = const()[name = tensor("op_7716"), val = tensor(0x1.4f8b58p-17)]; + tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; + tensor input_177 = layer_norm(axes = input_177_axes_0, beta = norm17_2_bias, epsilon = var_7716, gamma = norm17_2_weight, x = input_175)[name = tensor("input_177")]; + tensor var_7724 = linear(bias = linear_2_bias_0, weight = linear17_1_weight, x = input_177)[name = tensor("linear_70")]; + tensor input_179_mode_0 = const()[name = tensor("input_179_mode_0"), val = tensor("EXACT")]; + tensor input_179 = gelu(mode = input_179_mode_0, x = var_7724)[name = tensor("input_179")]; + tensor ffn_out_35 = linear(bias = linear_1_bias_0, weight = linear17_2_weight, x = input_179)[name = tensor("linear_71")]; + tensor input_181 = add(x = input_175, y = ffn_out_35)[name = tensor("input_181")]; + tensor var_7733 = const()[name = tensor("op_7733"), val = tensor(0x1.4f8b58p-17)]; + tensor x_37_axes_0 = const()[name = tensor("x_37_axes_0"), val = tensor([-1])]; + tensor x_37 = layer_norm(axes = x_37_axes_0, beta = norm18_1_bias, epsilon = var_7733, gamma = norm18_1_weight, x = input_181)[name = tensor("x_37")]; + tensor var_7765 = linear(bias = linear_0_bias_0, weight = attn18_in_proj_weight, x = x_37)[name = tensor("linear_72")]; + tensor var_7769 = const()[name = tensor("op_7769"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_37 = reshape(shape = var_7769, x = var_7765)[name = tensor("qkv_37")]; + tensor q_109_begin_0 = const()[name = tensor("q_109_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_109_end_0 = const()[name = tensor("q_109_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_109_end_mask_0 = const()[name = tensor("q_109_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_109_squeeze_mask_0 = const()[name = tensor("q_109_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_109 = slice_by_index(begin = q_109_begin_0, end = q_109_end_0, end_mask = q_109_end_mask_0, squeeze_mask = q_109_squeeze_mask_0, x = qkv_37)[name = tensor("q_109")]; + tensor k_73_begin_0 = const()[name = tensor("k_73_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_73_end_0 = const()[name = tensor("k_73_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_73_end_mask_0 = const()[name = tensor("k_73_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_73_squeeze_mask_0 = const()[name = tensor("k_73_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_73 = slice_by_index(begin = k_73_begin_0, end = k_73_end_0, end_mask = k_73_end_mask_0, squeeze_mask = k_73_squeeze_mask_0, x = qkv_37)[name = tensor("k_73")]; + tensor v_37_begin_0 = const()[name = tensor("v_37_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_37_end_0 = const()[name = tensor("v_37_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_37_end_mask_0 = const()[name = tensor("v_37_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_37_squeeze_mask_0 = const()[name = tensor("v_37_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_37 = slice_by_index(begin = v_37_begin_0, end = v_37_end_0, end_mask = v_37_end_mask_0, squeeze_mask = v_37_squeeze_mask_0, x = qkv_37)[name = tensor("v_37")]; + tensor freqs_37 = const()[name = tensor("freqs_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172743616)))]; + tensor var_7873 = const()[name = tensor("op_7873"), val = tensor([1, 1, 1, 1])]; + tensor ts_113 = reshape(shape = var_7873, x = position18)[name = tensor("ts_113")]; + tensor var_7877 = const()[name = tensor("op_7877"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_37 = reshape(shape = var_7877, x = q_109)[name = tensor("q_complex_37")]; + tensor var_7881 = const()[name = tensor("op_7881"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_37 = reshape(shape = var_7881, x = k_73)[name = tensor("k_complex_37")]; + tensor var_7885_begin_0 = const()[name = tensor("op_7885_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7885_end_0 = const()[name = tensor("op_7885_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7885_end_mask_0 = const()[name = tensor("op_7885_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7885_squeeze_mask_0 = const()[name = tensor("op_7885_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7885 = slice_by_index(begin = var_7885_begin_0, end = var_7885_end_0, end_mask = var_7885_end_mask_0, squeeze_mask = var_7885_squeeze_mask_0, x = q_complex_37)[name = tensor("op_7885")]; + tensor var_7893_begin_0 = const()[name = tensor("op_7893_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7893_end_0 = const()[name = tensor("op_7893_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7893_end_mask_0 = const()[name = tensor("op_7893_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7893_squeeze_mask_0 = const()[name = tensor("op_7893_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7893 = slice_by_index(begin = var_7893_begin_0, end = var_7893_end_0, end_mask = var_7893_end_mask_0, squeeze_mask = var_7893_squeeze_mask_0, x = q_complex_37)[name = tensor("op_7893")]; + tensor var_7901_begin_0 = const()[name = tensor("op_7901_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7901_end_0 = const()[name = tensor("op_7901_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7901_end_mask_0 = const()[name = tensor("op_7901_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7901_squeeze_mask_0 = const()[name = tensor("op_7901_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7901 = slice_by_index(begin = var_7901_begin_0, end = var_7901_end_0, end_mask = var_7901_end_mask_0, squeeze_mask = var_7901_squeeze_mask_0, x = k_complex_37)[name = tensor("op_7901")]; + tensor var_7909_begin_0 = const()[name = tensor("op_7909_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7909_end_0 = const()[name = tensor("op_7909_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7909_end_mask_0 = const()[name = tensor("op_7909_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7909_squeeze_mask_0 = const()[name = tensor("op_7909_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7909 = slice_by_index(begin = var_7909_begin_0, end = var_7909_end_0, end_mask = var_7909_end_mask_0, squeeze_mask = var_7909_squeeze_mask_0, x = k_complex_37)[name = tensor("op_7909")]; + tensor var_7915 = mul(x = freqs_37, y = ts_113)[name = tensor("op_7915")]; + tensor rotr_37 = cos(x = var_7915)[name = tensor("rotr_37")]; + tensor roti_37 = sin(x = var_7915)[name = tensor("roti_37")]; + tensor var_7919 = mul(x = var_7885, y = rotr_37)[name = tensor("op_7919")]; + tensor var_7920 = mul(x = var_7893, y = roti_37)[name = tensor("op_7920")]; + tensor qor_73 = sub(x = var_7919, y = var_7920)[name = tensor("qor_73")]; + tensor var_7923 = mul(x = var_7885, y = roti_37)[name = tensor("op_7923")]; + tensor var_7924 = mul(x = var_7893, y = rotr_37)[name = tensor("op_7924")]; + tensor qoi_73 = add(x = var_7923, y = var_7924)[name = tensor("qoi_73")]; + tensor var_7927 = mul(x = var_7901, y = rotr_37)[name = tensor("op_7927")]; + tensor var_7928 = mul(x = var_7909, y = roti_37)[name = tensor("op_7928")]; + tensor kor_73 = sub(x = var_7927, y = var_7928)[name = tensor("kor_73")]; + tensor var_7931 = mul(x = var_7901, y = roti_37)[name = tensor("op_7931")]; + tensor var_7932 = mul(x = var_7909, y = rotr_37)[name = tensor("op_7932")]; + tensor koi_73 = add(x = var_7931, y = var_7932)[name = tensor("koi_73")]; + tensor qo_37_axis_0 = const()[name = tensor("qo_37_axis_0"), val = tensor(-1)]; + tensor qo_37 = stack(axis = qo_37_axis_0, values = (qor_73, qoi_73))[name = tensor("qo_37")]; + tensor ko_37_axis_0 = const()[name = tensor("ko_37_axis_0"), val = tensor(-1)]; + tensor ko_37 = stack(axis = ko_37_axis_0, values = (kor_73, koi_73))[name = tensor("ko_37")]; + tensor var_7961 = const()[name = tensor("op_7961"), val = tensor([1, 1, 16, 64])]; + tensor q_111 = reshape(shape = var_7961, x = qo_37)[name = tensor("q_111")]; + tensor var_7963 = const()[name = tensor("op_7963"), val = tensor([1, 1, 16, 64])]; + tensor k_75 = reshape(shape = var_7963, x = ko_37)[name = tensor("k_75")]; + tensor _inversed_7985_y_0 = const()[name = tensor("_inversed_7985_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_7985 = mul(x = ts_113, y = _inversed_7985_y_0)[name = tensor("_inversed_7985")]; + tensor var_7986 = floor(x = _inversed_7985)[name = tensor("op_7986")]; + tensor var_7987 = const()[name = tensor("op_7987"), val = tensor(0x1p+9)]; + tensor var_7988 = mul(x = var_7986, y = var_7987)[name = tensor("op_7988")]; + tensor write_indices_float_75 = sub(x = ts_113, y = var_7988)[name = tensor("write_indices_float_75")]; + tensor var_7995_dtype_0 = const()[name = tensor("op_7995_dtype_0"), val = tensor("int32")]; + tensor write_indices_37_reps_0 = const()[name = tensor("write_indices_37_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_7995 = cast(dtype = var_7995_dtype_0, x = write_indices_float_75)[name = tensor("cast_428")]; + tensor write_indices_37 = tile(reps = write_indices_37_reps_0, x = var_7995)[name = tensor("write_indices_37")]; + tensor var_8003_begin_0 = const()[name = tensor("op_8003_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8003_end_0 = const()[name = tensor("op_8003_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_8003_end_mask_0 = const()[name = tensor("op_8003_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8003_squeeze_mask_0 = const()[name = tensor("op_8003_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8003 = slice_by_index(begin = var_8003_begin_0, end = var_8003_end_0, end_mask = var_8003_end_mask_0, squeeze_mask = var_8003_squeeze_mask_0, x = cache18)[name = tensor("op_8003")]; + tensor var_8005_axis_0 = const()[name = tensor("op_8005_axis_0"), val = tensor(1)]; + tensor var_8005_mode_0 = const()[name = tensor("op_8005_mode_0"), val = tensor("update")]; + tensor var_8005_validate_indices_0 = const()[name = tensor("op_8005_validate_indices_0"), val = tensor(false)]; + tensor var_8005 = scatter_along_axis(axis = var_8005_axis_0, data = var_8003, indices = write_indices_37, mode = var_8005_mode_0, updates = k_75, validate_indices = var_8005_validate_indices_0)[name = tensor("op_8005")]; + tensor concat_127 = const()[name = tensor("concat_127"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_128 = const()[name = tensor("concat_128"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_37_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_37_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_82 = const()[name = tensor("shape_82"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_36 = const()[name = tensor("reduce_prod_36"), val = tensor(1048576)]; + tensor range_1d_36_start_0 = const()[name = tensor("range_1d_36_start_0"), val = tensor(0)]; + tensor range_1d_36_step_0 = const()[name = tensor("range_1d_36_step_0"), val = tensor(1)]; + tensor range_1d_36 = range_1d(end = reduce_prod_36, start = range_1d_36_start_0, step = range_1d_36_step_0)[name = tensor("range_1d_36")]; + tensor reshape_180 = reshape(shape = shape_82, x = range_1d_36)[name = tensor("reshape_180")]; + tensor slice_by_index_36 = slice_by_index(begin = concat_127, begin_mask = new_cache_37_internal_tensor_assign_1_begin_mask_0, end = concat_128, end_mask = new_cache_37_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_37_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_37_internal_tensor_assign_1_stride_0, x = reshape_180)[name = tensor("slice_by_index_36")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([-1])]; + tensor reshape_181 = reshape(shape = reshape_181_shape_0, x = slice_by_index_36)[name = tensor("reshape_181")]; + tensor reshape_182_shape_0 = const()[name = tensor("reshape_182_shape_0"), val = tensor([-1])]; + tensor reshape_182 = reshape(shape = reshape_182_shape_0, x = var_8005)[name = tensor("reshape_182")]; + tensor reshape_183_shape_0 = const()[name = tensor("reshape_183_shape_0"), val = tensor([-1])]; + tensor reshape_183 = reshape(shape = reshape_183_shape_0, x = cache18)[name = tensor("reshape_183")]; + tensor scatter_36_mode_0 = const()[name = tensor("scatter_36_mode_0"), val = tensor("update")]; + tensor scatter_36_axis_0 = const()[name = tensor("scatter_36_axis_0"), val = tensor(0)]; + tensor scatter_36_validate_indices_0 = const()[name = tensor("scatter_36_validate_indices_0"), val = tensor(false)]; + tensor scatter_36 = scatter(axis = scatter_36_axis_0, data = reshape_183, indices = reshape_181, mode = scatter_36_mode_0, updates = reshape_182, validate_indices = scatter_36_validate_indices_0)[name = tensor("scatter_36")]; + tensor reshape_184 = reshape(shape = shape_82, x = scatter_36)[name = tensor("reshape_184")]; + tensor var_8013_begin_0 = const()[name = tensor("op_8013_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_8013_end_0 = const()[name = tensor("op_8013_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_8013_end_mask_0 = const()[name = tensor("op_8013_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8013_squeeze_mask_0 = const()[name = tensor("op_8013_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8013 = slice_by_index(begin = var_8013_begin_0, end = var_8013_end_0, end_mask = var_8013_end_mask_0, squeeze_mask = var_8013_squeeze_mask_0, x = reshape_184)[name = tensor("op_8013")]; + tensor var_8015_axis_0 = const()[name = tensor("op_8015_axis_0"), val = tensor(1)]; + tensor var_8015_mode_0 = const()[name = tensor("op_8015_mode_0"), val = tensor("update")]; + tensor var_8015_validate_indices_0 = const()[name = tensor("op_8015_validate_indices_0"), val = tensor(false)]; + tensor var_8015 = scatter_along_axis(axis = var_8015_axis_0, data = var_8013, indices = write_indices_37, mode = var_8015_mode_0, updates = v_37, validate_indices = var_8015_validate_indices_0)[name = tensor("op_8015")]; + tensor concat_129 = const()[name = tensor("concat_129"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_130 = const()[name = tensor("concat_130"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_37_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_37_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_83 = const()[name = tensor("shape_83"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_37 = const()[name = tensor("reduce_prod_37"), val = tensor(1048576)]; + tensor range_1d_37_start_0 = const()[name = tensor("range_1d_37_start_0"), val = tensor(0)]; + tensor range_1d_37_step_0 = const()[name = tensor("range_1d_37_step_0"), val = tensor(1)]; + tensor range_1d_37 = range_1d(end = reduce_prod_37, start = range_1d_37_start_0, step = range_1d_37_step_0)[name = tensor("range_1d_37")]; + tensor reshape_185 = reshape(shape = shape_83, x = range_1d_37)[name = tensor("reshape_185")]; + tensor slice_by_index_37 = slice_by_index(begin = concat_129, begin_mask = new_cache_37_internal_tensor_assign_2_begin_mask_0, end = concat_130, end_mask = new_cache_37_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_37_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_37_internal_tensor_assign_2_stride_0, x = reshape_185)[name = tensor("slice_by_index_37")]; + tensor reshape_186_shape_0 = const()[name = tensor("reshape_186_shape_0"), val = tensor([-1])]; + tensor reshape_186 = reshape(shape = reshape_186_shape_0, x = slice_by_index_37)[name = tensor("reshape_186")]; + tensor reshape_187_shape_0 = const()[name = tensor("reshape_187_shape_0"), val = tensor([-1])]; + tensor reshape_187 = reshape(shape = reshape_187_shape_0, x = var_8015)[name = tensor("reshape_187")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([-1])]; + tensor reshape_188 = reshape(shape = reshape_188_shape_0, x = reshape_184)[name = tensor("reshape_188")]; + tensor scatter_37_mode_0 = const()[name = tensor("scatter_37_mode_0"), val = tensor("update")]; + tensor scatter_37_axis_0 = const()[name = tensor("scatter_37_axis_0"), val = tensor(0)]; + tensor scatter_37_validate_indices_0 = const()[name = tensor("scatter_37_validate_indices_0"), val = tensor(false)]; + tensor scatter_37 = scatter(axis = scatter_37_axis_0, data = reshape_188, indices = reshape_186, mode = scatter_37_mode_0, updates = reshape_187, validate_indices = scatter_37_validate_indices_0)[name = tensor("scatter_37")]; + tensor new_cache_37_internal_tensor_assign_2 = reshape(shape = shape_83, x = scatter_37)[name = tensor("reshape_189")]; + tensor keys_109_begin_0 = const()[name = tensor("keys_109_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_109_end_0 = const()[name = tensor("keys_109_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_109_end_mask_0 = const()[name = tensor("keys_109_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_109_squeeze_mask_0 = const()[name = tensor("keys_109_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_109 = slice_by_index(begin = keys_109_begin_0, end = keys_109_end_0, end_mask = keys_109_end_mask_0, squeeze_mask = keys_109_squeeze_mask_0, x = new_cache_37_internal_tensor_assign_2)[name = tensor("keys_109")]; + tensor values_109_begin_0 = const()[name = tensor("values_109_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_109_end_0 = const()[name = tensor("values_109_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_109_end_mask_0 = const()[name = tensor("values_109_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_109_squeeze_mask_0 = const()[name = tensor("values_109_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_109 = slice_by_index(begin = values_109_begin_0, end = values_109_end_0, end_mask = values_109_end_mask_0, squeeze_mask = values_109_squeeze_mask_0, x = new_cache_37_internal_tensor_assign_2)[name = tensor("values_109")]; + tensor var_8027 = not_equal(x = keys_109, y = keys_109)[name = tensor("op_8027")]; + tensor keys_111 = select(a = var_491, b = keys_109, cond = var_8027)[name = tensor("keys_111")]; + tensor var_8035 = not_equal(x = values_109, y = values_109)[name = tensor("op_8035")]; + tensor values_111 = select(a = var_491, b = values_109, cond = var_8035)[name = tensor("values_111")]; + tensor var_8059 = const()[name = tensor("op_8059"), val = tensor([0, 2, 1, 3])]; + tensor var_8072 = const()[name = tensor("op_8072"), val = tensor([1, 1, 1])]; + tensor var_8073 = reshape(shape = var_8072, x = position18)[name = tensor("op_8073")]; + tensor var_8090 = const()[name = tensor("op_8090"), val = tensor(0x1p+0)]; + tensor valid_len_37 = add(x = var_8073, y = var_8090)[name = tensor("valid_len_37")]; + tensor valid_mask_37 = less(x = k_positions_1_promoted, y = valid_len_37)[name = tensor("valid_mask_37")]; + tensor causal_mask_37 = less_equal(x = k_positions_1_promoted, y = var_8073)[name = tensor("causal_mask_37")]; + tensor attn_mask_73 = logical_and(x = valid_mask_37, y = causal_mask_37)[name = tensor("attn_mask_73")]; + tensor attn_mask_75_axes_0 = const()[name = tensor("attn_mask_75_axes_0"), val = tensor([1])]; + tensor attn_mask_75 = expand_dims(axes = attn_mask_75_axes_0, x = attn_mask_73)[name = tensor("attn_mask_75")]; + tensor var_8102 = const()[name = tensor("op_8102"), val = tensor([0x1.fffe5cp-4])]; + tensor var_8108_transpose_x_0 = const()[name = tensor("op_8108_transpose_x_0"), val = tensor(false)]; + tensor var_8108_transpose_y_0 = const()[name = tensor("op_8108_transpose_y_0"), val = tensor(false)]; + tensor transpose_105_perm_0 = const()[name = tensor("transpose_105_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_106_perm_0 = const()[name = tensor("transpose_106_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = keys_111)[name = tensor("transpose_132")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = q_111)[name = tensor("transpose_133")]; + tensor var_8108 = matmul(transpose_x = var_8108_transpose_x_0, transpose_y = var_8108_transpose_y_0, x = transpose_105, y = transpose_106)[name = tensor("op_8108")]; + tensor attn_weights_109 = mul(x = var_8108, y = var_8102)[name = tensor("attn_weights_109")]; + tensor var_8110 = logical_not(x = attn_mask_75)[name = tensor("op_8110")]; + tensor var_8111 = const()[name = tensor("op_8111"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_111 = select(a = var_8111, b = attn_weights_109, cond = var_8110)[name = tensor("attn_weights_111")]; + tensor var_8113 = const()[name = tensor("op_8113"), val = tensor(-1)]; + tensor attn_weights_113 = softmax(axis = var_8113, x = attn_weights_111)[name = tensor("attn_weights_113")]; + tensor attn_output_37_transpose_x_0 = const()[name = tensor("attn_output_37_transpose_x_0"), val = tensor(false)]; + tensor attn_output_37_transpose_y_0 = const()[name = tensor("attn_output_37_transpose_y_0"), val = tensor(false)]; + tensor values_113 = transpose(perm = var_8059, x = values_111)[name = tensor("transpose_134")]; + tensor attn_output_37 = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = attn_weights_113, y = values_113)[name = tensor("attn_output_37")]; + tensor var_8121 = const()[name = tensor("op_8121"), val = tensor([0, 2, 1, 3])]; + tensor var_8124 = const()[name = tensor("op_8124"), val = tensor([1, 1, 1024])]; + tensor var_8122 = transpose(perm = var_8121, x = attn_output_37)[name = tensor("transpose_131")]; + tensor input_183 = reshape(shape = var_8124, x = var_8122)[name = tensor("input_183")]; + tensor attn_out_37 = linear(bias = linear_1_bias_0, weight = attn18_out_proj_weight, x = input_183)[name = tensor("linear_73")]; + tensor var_8130 = const()[name = tensor("op_8130"), val = tensor(0x1p+0)]; + tensor var_8131 = add(x = position18, y = var_8130)[name = tensor("op_8131")]; + tensor input_185 = add(x = input_181, y = attn_out_37)[name = tensor("input_185")]; + tensor var_8135 = const()[name = tensor("op_8135"), val = tensor(0x1.4f8b58p-17)]; + tensor input_187_axes_0 = const()[name = tensor("input_187_axes_0"), val = tensor([-1])]; + tensor input_187 = layer_norm(axes = input_187_axes_0, beta = norm18_2_bias, epsilon = var_8135, gamma = norm18_2_weight, x = input_185)[name = tensor("input_187")]; + tensor var_8143 = linear(bias = linear_2_bias_0, weight = linear18_1_weight, x = input_187)[name = tensor("linear_74")]; + tensor input_189_mode_0 = const()[name = tensor("input_189_mode_0"), val = tensor("EXACT")]; + tensor input_189 = gelu(mode = input_189_mode_0, x = var_8143)[name = tensor("input_189")]; + tensor ffn_out_37 = linear(bias = linear_1_bias_0, weight = linear18_2_weight, x = input_189)[name = tensor("linear_75")]; + tensor input_191 = add(x = input_185, y = ffn_out_37)[name = tensor("input_191")]; + tensor var_8152 = const()[name = tensor("op_8152"), val = tensor(0x1.4f8b58p-17)]; + tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; + tensor x_39 = layer_norm(axes = x_39_axes_0, beta = norm19_1_bias, epsilon = var_8152, gamma = norm19_1_weight, x = input_191)[name = tensor("x_39")]; + tensor var_8184 = linear(bias = linear_0_bias_0, weight = attn19_in_proj_weight, x = x_39)[name = tensor("linear_76")]; + tensor var_8188 = const()[name = tensor("op_8188"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_39 = reshape(shape = var_8188, x = var_8184)[name = tensor("qkv_39")]; + tensor q_115_begin_0 = const()[name = tensor("q_115_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_115_end_0 = const()[name = tensor("q_115_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_115_end_mask_0 = const()[name = tensor("q_115_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_115_squeeze_mask_0 = const()[name = tensor("q_115_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_115 = slice_by_index(begin = q_115_begin_0, end = q_115_end_0, end_mask = q_115_end_mask_0, squeeze_mask = q_115_squeeze_mask_0, x = qkv_39)[name = tensor("q_115")]; + tensor k_77_begin_0 = const()[name = tensor("k_77_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_77_end_0 = const()[name = tensor("k_77_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_77_end_mask_0 = const()[name = tensor("k_77_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_77_squeeze_mask_0 = const()[name = tensor("k_77_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_77 = slice_by_index(begin = k_77_begin_0, end = k_77_end_0, end_mask = k_77_end_mask_0, squeeze_mask = k_77_squeeze_mask_0, x = qkv_39)[name = tensor("k_77")]; + tensor v_39_begin_0 = const()[name = tensor("v_39_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_39_end_0 = const()[name = tensor("v_39_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_39_end_mask_0 = const()[name = tensor("v_39_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_39_squeeze_mask_0 = const()[name = tensor("v_39_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_39 = slice_by_index(begin = v_39_begin_0, end = v_39_end_0, end_mask = v_39_end_mask_0, squeeze_mask = v_39_squeeze_mask_0, x = qkv_39)[name = tensor("v_39")]; + tensor freqs_39 = const()[name = tensor("freqs_39"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172743808)))]; + tensor var_8292 = const()[name = tensor("op_8292"), val = tensor([1, 1, 1, 1])]; + tensor ts_119 = reshape(shape = var_8292, x = position19)[name = tensor("ts_119")]; + tensor var_8296 = const()[name = tensor("op_8296"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_39 = reshape(shape = var_8296, x = q_115)[name = tensor("q_complex_39")]; + tensor var_8300 = const()[name = tensor("op_8300"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_39 = reshape(shape = var_8300, x = k_77)[name = tensor("k_complex_39")]; + tensor var_8304_begin_0 = const()[name = tensor("op_8304_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8304_end_0 = const()[name = tensor("op_8304_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8304_end_mask_0 = const()[name = tensor("op_8304_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8304_squeeze_mask_0 = const()[name = tensor("op_8304_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8304 = slice_by_index(begin = var_8304_begin_0, end = var_8304_end_0, end_mask = var_8304_end_mask_0, squeeze_mask = var_8304_squeeze_mask_0, x = q_complex_39)[name = tensor("op_8304")]; + tensor var_8312_begin_0 = const()[name = tensor("op_8312_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8312_end_0 = const()[name = tensor("op_8312_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8312_end_mask_0 = const()[name = tensor("op_8312_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8312_squeeze_mask_0 = const()[name = tensor("op_8312_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8312 = slice_by_index(begin = var_8312_begin_0, end = var_8312_end_0, end_mask = var_8312_end_mask_0, squeeze_mask = var_8312_squeeze_mask_0, x = q_complex_39)[name = tensor("op_8312")]; + tensor var_8320_begin_0 = const()[name = tensor("op_8320_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8320_end_0 = const()[name = tensor("op_8320_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8320_end_mask_0 = const()[name = tensor("op_8320_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8320_squeeze_mask_0 = const()[name = tensor("op_8320_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8320 = slice_by_index(begin = var_8320_begin_0, end = var_8320_end_0, end_mask = var_8320_end_mask_0, squeeze_mask = var_8320_squeeze_mask_0, x = k_complex_39)[name = tensor("op_8320")]; + tensor var_8328_begin_0 = const()[name = tensor("op_8328_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8328_end_0 = const()[name = tensor("op_8328_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8328_end_mask_0 = const()[name = tensor("op_8328_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8328_squeeze_mask_0 = const()[name = tensor("op_8328_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8328 = slice_by_index(begin = var_8328_begin_0, end = var_8328_end_0, end_mask = var_8328_end_mask_0, squeeze_mask = var_8328_squeeze_mask_0, x = k_complex_39)[name = tensor("op_8328")]; + tensor var_8334 = mul(x = freqs_39, y = ts_119)[name = tensor("op_8334")]; + tensor rotr_39 = cos(x = var_8334)[name = tensor("rotr_39")]; + tensor roti_39 = sin(x = var_8334)[name = tensor("roti_39")]; + tensor var_8338 = mul(x = var_8304, y = rotr_39)[name = tensor("op_8338")]; + tensor var_8339 = mul(x = var_8312, y = roti_39)[name = tensor("op_8339")]; + tensor qor_77 = sub(x = var_8338, y = var_8339)[name = tensor("qor_77")]; + tensor var_8342 = mul(x = var_8304, y = roti_39)[name = tensor("op_8342")]; + tensor var_8343 = mul(x = var_8312, y = rotr_39)[name = tensor("op_8343")]; + tensor qoi_77 = add(x = var_8342, y = var_8343)[name = tensor("qoi_77")]; + tensor var_8346 = mul(x = var_8320, y = rotr_39)[name = tensor("op_8346")]; + tensor var_8347 = mul(x = var_8328, y = roti_39)[name = tensor("op_8347")]; + tensor kor_77 = sub(x = var_8346, y = var_8347)[name = tensor("kor_77")]; + tensor var_8350 = mul(x = var_8320, y = roti_39)[name = tensor("op_8350")]; + tensor var_8351 = mul(x = var_8328, y = rotr_39)[name = tensor("op_8351")]; + tensor koi_77 = add(x = var_8350, y = var_8351)[name = tensor("koi_77")]; + tensor qo_39_axis_0 = const()[name = tensor("qo_39_axis_0"), val = tensor(-1)]; + tensor qo_39 = stack(axis = qo_39_axis_0, values = (qor_77, qoi_77))[name = tensor("qo_39")]; + tensor ko_39_axis_0 = const()[name = tensor("ko_39_axis_0"), val = tensor(-1)]; + tensor ko_39 = stack(axis = ko_39_axis_0, values = (kor_77, koi_77))[name = tensor("ko_39")]; + tensor var_8380 = const()[name = tensor("op_8380"), val = tensor([1, 1, 16, 64])]; + tensor q_117 = reshape(shape = var_8380, x = qo_39)[name = tensor("q_117")]; + tensor var_8382 = const()[name = tensor("op_8382"), val = tensor([1, 1, 16, 64])]; + tensor k_79 = reshape(shape = var_8382, x = ko_39)[name = tensor("k_79")]; + tensor _inversed_8404_y_0 = const()[name = tensor("_inversed_8404_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_8404 = mul(x = ts_119, y = _inversed_8404_y_0)[name = tensor("_inversed_8404")]; + tensor var_8405 = floor(x = _inversed_8404)[name = tensor("op_8405")]; + tensor var_8406 = const()[name = tensor("op_8406"), val = tensor(0x1p+9)]; + tensor var_8407 = mul(x = var_8405, y = var_8406)[name = tensor("op_8407")]; + tensor write_indices_float_79 = sub(x = ts_119, y = var_8407)[name = tensor("write_indices_float_79")]; + tensor var_8414_dtype_0 = const()[name = tensor("op_8414_dtype_0"), val = tensor("int32")]; + tensor write_indices_39_reps_0 = const()[name = tensor("write_indices_39_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_8414 = cast(dtype = var_8414_dtype_0, x = write_indices_float_79)[name = tensor("cast_427")]; + tensor write_indices_39 = tile(reps = write_indices_39_reps_0, x = var_8414)[name = tensor("write_indices_39")]; + tensor var_8422_begin_0 = const()[name = tensor("op_8422_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8422_end_0 = const()[name = tensor("op_8422_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_8422_end_mask_0 = const()[name = tensor("op_8422_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8422_squeeze_mask_0 = const()[name = tensor("op_8422_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8422 = slice_by_index(begin = var_8422_begin_0, end = var_8422_end_0, end_mask = var_8422_end_mask_0, squeeze_mask = var_8422_squeeze_mask_0, x = cache19)[name = tensor("op_8422")]; + tensor var_8424_axis_0 = const()[name = tensor("op_8424_axis_0"), val = tensor(1)]; + tensor var_8424_mode_0 = const()[name = tensor("op_8424_mode_0"), val = tensor("update")]; + tensor var_8424_validate_indices_0 = const()[name = tensor("op_8424_validate_indices_0"), val = tensor(false)]; + tensor var_8424 = scatter_along_axis(axis = var_8424_axis_0, data = var_8422, indices = write_indices_39, mode = var_8424_mode_0, updates = k_79, validate_indices = var_8424_validate_indices_0)[name = tensor("op_8424")]; + tensor concat_134 = const()[name = tensor("concat_134"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_135 = const()[name = tensor("concat_135"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_39_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_39_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_84 = const()[name = tensor("shape_84"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_38 = const()[name = tensor("reduce_prod_38"), val = tensor(1048576)]; + tensor range_1d_38_start_0 = const()[name = tensor("range_1d_38_start_0"), val = tensor(0)]; + tensor range_1d_38_step_0 = const()[name = tensor("range_1d_38_step_0"), val = tensor(1)]; + tensor range_1d_38 = range_1d(end = reduce_prod_38, start = range_1d_38_start_0, step = range_1d_38_step_0)[name = tensor("range_1d_38")]; + tensor reshape_190 = reshape(shape = shape_84, x = range_1d_38)[name = tensor("reshape_190")]; + tensor slice_by_index_38 = slice_by_index(begin = concat_134, begin_mask = new_cache_39_internal_tensor_assign_1_begin_mask_0, end = concat_135, end_mask = new_cache_39_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_39_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_39_internal_tensor_assign_1_stride_0, x = reshape_190)[name = tensor("slice_by_index_38")]; + tensor reshape_191_shape_0 = const()[name = tensor("reshape_191_shape_0"), val = tensor([-1])]; + tensor reshape_191 = reshape(shape = reshape_191_shape_0, x = slice_by_index_38)[name = tensor("reshape_191")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([-1])]; + tensor reshape_192 = reshape(shape = reshape_192_shape_0, x = var_8424)[name = tensor("reshape_192")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([-1])]; + tensor reshape_193 = reshape(shape = reshape_193_shape_0, x = cache19)[name = tensor("reshape_193")]; + tensor scatter_38_mode_0 = const()[name = tensor("scatter_38_mode_0"), val = tensor("update")]; + tensor scatter_38_axis_0 = const()[name = tensor("scatter_38_axis_0"), val = tensor(0)]; + tensor scatter_38_validate_indices_0 = const()[name = tensor("scatter_38_validate_indices_0"), val = tensor(false)]; + tensor scatter_38 = scatter(axis = scatter_38_axis_0, data = reshape_193, indices = reshape_191, mode = scatter_38_mode_0, updates = reshape_192, validate_indices = scatter_38_validate_indices_0)[name = tensor("scatter_38")]; + tensor reshape_194 = reshape(shape = shape_84, x = scatter_38)[name = tensor("reshape_194")]; + tensor var_8432_begin_0 = const()[name = tensor("op_8432_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_8432_end_0 = const()[name = tensor("op_8432_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_8432_end_mask_0 = const()[name = tensor("op_8432_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8432_squeeze_mask_0 = const()[name = tensor("op_8432_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8432 = slice_by_index(begin = var_8432_begin_0, end = var_8432_end_0, end_mask = var_8432_end_mask_0, squeeze_mask = var_8432_squeeze_mask_0, x = reshape_194)[name = tensor("op_8432")]; + tensor var_8434_axis_0 = const()[name = tensor("op_8434_axis_0"), val = tensor(1)]; + tensor var_8434_mode_0 = const()[name = tensor("op_8434_mode_0"), val = tensor("update")]; + tensor var_8434_validate_indices_0 = const()[name = tensor("op_8434_validate_indices_0"), val = tensor(false)]; + tensor var_8434 = scatter_along_axis(axis = var_8434_axis_0, data = var_8432, indices = write_indices_39, mode = var_8434_mode_0, updates = v_39, validate_indices = var_8434_validate_indices_0)[name = tensor("op_8434")]; + tensor concat_136 = const()[name = tensor("concat_136"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_137 = const()[name = tensor("concat_137"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_39_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_39_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_85 = const()[name = tensor("shape_85"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_39 = const()[name = tensor("reduce_prod_39"), val = tensor(1048576)]; + tensor range_1d_39_start_0 = const()[name = tensor("range_1d_39_start_0"), val = tensor(0)]; + tensor range_1d_39_step_0 = const()[name = tensor("range_1d_39_step_0"), val = tensor(1)]; + tensor range_1d_39 = range_1d(end = reduce_prod_39, start = range_1d_39_start_0, step = range_1d_39_step_0)[name = tensor("range_1d_39")]; + tensor reshape_195 = reshape(shape = shape_85, x = range_1d_39)[name = tensor("reshape_195")]; + tensor slice_by_index_39 = slice_by_index(begin = concat_136, begin_mask = new_cache_39_internal_tensor_assign_2_begin_mask_0, end = concat_137, end_mask = new_cache_39_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_39_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_39_internal_tensor_assign_2_stride_0, x = reshape_195)[name = tensor("slice_by_index_39")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([-1])]; + tensor reshape_196 = reshape(shape = reshape_196_shape_0, x = slice_by_index_39)[name = tensor("reshape_196")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([-1])]; + tensor reshape_197 = reshape(shape = reshape_197_shape_0, x = var_8434)[name = tensor("reshape_197")]; + tensor reshape_198_shape_0 = const()[name = tensor("reshape_198_shape_0"), val = tensor([-1])]; + tensor reshape_198 = reshape(shape = reshape_198_shape_0, x = reshape_194)[name = tensor("reshape_198")]; + tensor scatter_39_mode_0 = const()[name = tensor("scatter_39_mode_0"), val = tensor("update")]; + tensor scatter_39_axis_0 = const()[name = tensor("scatter_39_axis_0"), val = tensor(0)]; + tensor scatter_39_validate_indices_0 = const()[name = tensor("scatter_39_validate_indices_0"), val = tensor(false)]; + tensor scatter_39 = scatter(axis = scatter_39_axis_0, data = reshape_198, indices = reshape_196, mode = scatter_39_mode_0, updates = reshape_197, validate_indices = scatter_39_validate_indices_0)[name = tensor("scatter_39")]; + tensor new_cache_39_internal_tensor_assign_2 = reshape(shape = shape_85, x = scatter_39)[name = tensor("reshape_199")]; + tensor keys_115_begin_0 = const()[name = tensor("keys_115_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_115_end_0 = const()[name = tensor("keys_115_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_115_end_mask_0 = const()[name = tensor("keys_115_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_115_squeeze_mask_0 = const()[name = tensor("keys_115_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_115 = slice_by_index(begin = keys_115_begin_0, end = keys_115_end_0, end_mask = keys_115_end_mask_0, squeeze_mask = keys_115_squeeze_mask_0, x = new_cache_39_internal_tensor_assign_2)[name = tensor("keys_115")]; + tensor values_115_begin_0 = const()[name = tensor("values_115_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_115_end_0 = const()[name = tensor("values_115_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_115_end_mask_0 = const()[name = tensor("values_115_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_115_squeeze_mask_0 = const()[name = tensor("values_115_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_115 = slice_by_index(begin = values_115_begin_0, end = values_115_end_0, end_mask = values_115_end_mask_0, squeeze_mask = values_115_squeeze_mask_0, x = new_cache_39_internal_tensor_assign_2)[name = tensor("values_115")]; + tensor var_8446 = not_equal(x = keys_115, y = keys_115)[name = tensor("op_8446")]; + tensor keys_117 = select(a = var_491, b = keys_115, cond = var_8446)[name = tensor("keys_117")]; + tensor var_8454 = not_equal(x = values_115, y = values_115)[name = tensor("op_8454")]; + tensor values_117 = select(a = var_491, b = values_115, cond = var_8454)[name = tensor("values_117")]; + tensor var_8478 = const()[name = tensor("op_8478"), val = tensor([0, 2, 1, 3])]; + tensor var_8491 = const()[name = tensor("op_8491"), val = tensor([1, 1, 1])]; + tensor var_8492 = reshape(shape = var_8491, x = position19)[name = tensor("op_8492")]; + tensor var_8509 = const()[name = tensor("op_8509"), val = tensor(0x1p+0)]; + tensor valid_len_39 = add(x = var_8492, y = var_8509)[name = tensor("valid_len_39")]; + tensor valid_mask_39 = less(x = k_positions_1_promoted, y = valid_len_39)[name = tensor("valid_mask_39")]; + tensor causal_mask_39 = less_equal(x = k_positions_1_promoted, y = var_8492)[name = tensor("causal_mask_39")]; + tensor attn_mask_77 = logical_and(x = valid_mask_39, y = causal_mask_39)[name = tensor("attn_mask_77")]; + tensor attn_mask_79_axes_0 = const()[name = tensor("attn_mask_79_axes_0"), val = tensor([1])]; + tensor attn_mask_79 = expand_dims(axes = attn_mask_79_axes_0, x = attn_mask_77)[name = tensor("attn_mask_79")]; + tensor var_8521 = const()[name = tensor("op_8521"), val = tensor([0x1.fffe5cp-4])]; + tensor var_8527_transpose_x_0 = const()[name = tensor("op_8527_transpose_x_0"), val = tensor(false)]; + tensor var_8527_transpose_y_0 = const()[name = tensor("op_8527_transpose_y_0"), val = tensor(false)]; + tensor transpose_107_perm_0 = const()[name = tensor("transpose_107_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_108_perm_0 = const()[name = tensor("transpose_108_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = keys_117)[name = tensor("transpose_128")]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = q_117)[name = tensor("transpose_129")]; + tensor var_8527 = matmul(transpose_x = var_8527_transpose_x_0, transpose_y = var_8527_transpose_y_0, x = transpose_107, y = transpose_108)[name = tensor("op_8527")]; + tensor attn_weights_115 = mul(x = var_8527, y = var_8521)[name = tensor("attn_weights_115")]; + tensor var_8529 = logical_not(x = attn_mask_79)[name = tensor("op_8529")]; + tensor var_8530 = const()[name = tensor("op_8530"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_117 = select(a = var_8530, b = attn_weights_115, cond = var_8529)[name = tensor("attn_weights_117")]; + tensor var_8532 = const()[name = tensor("op_8532"), val = tensor(-1)]; + tensor attn_weights_119 = softmax(axis = var_8532, x = attn_weights_117)[name = tensor("attn_weights_119")]; + tensor attn_output_39_transpose_x_0 = const()[name = tensor("attn_output_39_transpose_x_0"), val = tensor(false)]; + tensor attn_output_39_transpose_y_0 = const()[name = tensor("attn_output_39_transpose_y_0"), val = tensor(false)]; + tensor values_119 = transpose(perm = var_8478, x = values_117)[name = tensor("transpose_130")]; + tensor attn_output_39 = matmul(transpose_x = attn_output_39_transpose_x_0, transpose_y = attn_output_39_transpose_y_0, x = attn_weights_119, y = values_119)[name = tensor("attn_output_39")]; + tensor var_8540 = const()[name = tensor("op_8540"), val = tensor([0, 2, 1, 3])]; + tensor var_8543 = const()[name = tensor("op_8543"), val = tensor([1, 1, 1024])]; + tensor var_8541 = transpose(perm = var_8540, x = attn_output_39)[name = tensor("transpose_127")]; + tensor input_193 = reshape(shape = var_8543, x = var_8541)[name = tensor("input_193")]; + tensor attn_out_39 = linear(bias = linear_1_bias_0, weight = attn19_out_proj_weight, x = input_193)[name = tensor("linear_77")]; + tensor var_8549 = const()[name = tensor("op_8549"), val = tensor(0x1p+0)]; + tensor var_8550 = add(x = position19, y = var_8549)[name = tensor("op_8550")]; + tensor input_195 = add(x = input_191, y = attn_out_39)[name = tensor("input_195")]; + tensor var_8554 = const()[name = tensor("op_8554"), val = tensor(0x1.4f8b58p-17)]; + tensor input_197_axes_0 = const()[name = tensor("input_197_axes_0"), val = tensor([-1])]; + tensor input_197 = layer_norm(axes = input_197_axes_0, beta = norm19_2_bias, epsilon = var_8554, gamma = norm19_2_weight, x = input_195)[name = tensor("input_197")]; + tensor var_8562 = linear(bias = linear_2_bias_0, weight = linear19_1_weight, x = input_197)[name = tensor("linear_78")]; + tensor input_199_mode_0 = const()[name = tensor("input_199_mode_0"), val = tensor("EXACT")]; + tensor input_199 = gelu(mode = input_199_mode_0, x = var_8562)[name = tensor("input_199")]; + tensor ffn_out_39 = linear(bias = linear_1_bias_0, weight = linear19_2_weight, x = input_199)[name = tensor("linear_79")]; + tensor input_201 = add(x = input_195, y = ffn_out_39)[name = tensor("input_201")]; + tensor var_8571 = const()[name = tensor("op_8571"), val = tensor(0x1.4f8b58p-17)]; + tensor x_41_axes_0 = const()[name = tensor("x_41_axes_0"), val = tensor([-1])]; + tensor x_41 = layer_norm(axes = x_41_axes_0, beta = norm20_1_bias, epsilon = var_8571, gamma = norm20_1_weight, x = input_201)[name = tensor("x_41")]; + tensor var_8603 = linear(bias = linear_0_bias_0, weight = attn20_in_proj_weight, x = x_41)[name = tensor("linear_80")]; + tensor var_8607 = const()[name = tensor("op_8607"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_41 = reshape(shape = var_8607, x = var_8603)[name = tensor("qkv_41")]; + tensor q_121_begin_0 = const()[name = tensor("q_121_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_121_end_0 = const()[name = tensor("q_121_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_121_end_mask_0 = const()[name = tensor("q_121_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_121_squeeze_mask_0 = const()[name = tensor("q_121_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_121 = slice_by_index(begin = q_121_begin_0, end = q_121_end_0, end_mask = q_121_end_mask_0, squeeze_mask = q_121_squeeze_mask_0, x = qkv_41)[name = tensor("q_121")]; + tensor k_81_begin_0 = const()[name = tensor("k_81_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_81_end_0 = const()[name = tensor("k_81_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_81_end_mask_0 = const()[name = tensor("k_81_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_81_squeeze_mask_0 = const()[name = tensor("k_81_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_81 = slice_by_index(begin = k_81_begin_0, end = k_81_end_0, end_mask = k_81_end_mask_0, squeeze_mask = k_81_squeeze_mask_0, x = qkv_41)[name = tensor("k_81")]; + tensor v_41_begin_0 = const()[name = tensor("v_41_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_41_end_0 = const()[name = tensor("v_41_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_41_end_mask_0 = const()[name = tensor("v_41_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_41_squeeze_mask_0 = const()[name = tensor("v_41_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_41 = slice_by_index(begin = v_41_begin_0, end = v_41_end_0, end_mask = v_41_end_mask_0, squeeze_mask = v_41_squeeze_mask_0, x = qkv_41)[name = tensor("v_41")]; + tensor freqs_41 = const()[name = tensor("freqs_41"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172744000)))]; + tensor var_8711 = const()[name = tensor("op_8711"), val = tensor([1, 1, 1, 1])]; + tensor ts_125 = reshape(shape = var_8711, x = position20)[name = tensor("ts_125")]; + tensor var_8715 = const()[name = tensor("op_8715"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_41 = reshape(shape = var_8715, x = q_121)[name = tensor("q_complex_41")]; + tensor var_8719 = const()[name = tensor("op_8719"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_41 = reshape(shape = var_8719, x = k_81)[name = tensor("k_complex_41")]; + tensor var_8723_begin_0 = const()[name = tensor("op_8723_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8723_end_0 = const()[name = tensor("op_8723_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8723_end_mask_0 = const()[name = tensor("op_8723_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8723_squeeze_mask_0 = const()[name = tensor("op_8723_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8723 = slice_by_index(begin = var_8723_begin_0, end = var_8723_end_0, end_mask = var_8723_end_mask_0, squeeze_mask = var_8723_squeeze_mask_0, x = q_complex_41)[name = tensor("op_8723")]; + tensor var_8731_begin_0 = const()[name = tensor("op_8731_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8731_end_0 = const()[name = tensor("op_8731_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8731_end_mask_0 = const()[name = tensor("op_8731_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8731_squeeze_mask_0 = const()[name = tensor("op_8731_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8731 = slice_by_index(begin = var_8731_begin_0, end = var_8731_end_0, end_mask = var_8731_end_mask_0, squeeze_mask = var_8731_squeeze_mask_0, x = q_complex_41)[name = tensor("op_8731")]; + tensor var_8739_begin_0 = const()[name = tensor("op_8739_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8739_end_0 = const()[name = tensor("op_8739_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8739_end_mask_0 = const()[name = tensor("op_8739_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8739_squeeze_mask_0 = const()[name = tensor("op_8739_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8739 = slice_by_index(begin = var_8739_begin_0, end = var_8739_end_0, end_mask = var_8739_end_mask_0, squeeze_mask = var_8739_squeeze_mask_0, x = k_complex_41)[name = tensor("op_8739")]; + tensor var_8747_begin_0 = const()[name = tensor("op_8747_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8747_end_0 = const()[name = tensor("op_8747_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8747_end_mask_0 = const()[name = tensor("op_8747_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8747_squeeze_mask_0 = const()[name = tensor("op_8747_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8747 = slice_by_index(begin = var_8747_begin_0, end = var_8747_end_0, end_mask = var_8747_end_mask_0, squeeze_mask = var_8747_squeeze_mask_0, x = k_complex_41)[name = tensor("op_8747")]; + tensor var_8753 = mul(x = freqs_41, y = ts_125)[name = tensor("op_8753")]; + tensor rotr_41 = cos(x = var_8753)[name = tensor("rotr_41")]; + tensor roti_41 = sin(x = var_8753)[name = tensor("roti_41")]; + tensor var_8757 = mul(x = var_8723, y = rotr_41)[name = tensor("op_8757")]; + tensor var_8758 = mul(x = var_8731, y = roti_41)[name = tensor("op_8758")]; + tensor qor_81 = sub(x = var_8757, y = var_8758)[name = tensor("qor_81")]; + tensor var_8761 = mul(x = var_8723, y = roti_41)[name = tensor("op_8761")]; + tensor var_8762 = mul(x = var_8731, y = rotr_41)[name = tensor("op_8762")]; + tensor qoi_81 = add(x = var_8761, y = var_8762)[name = tensor("qoi_81")]; + tensor var_8765 = mul(x = var_8739, y = rotr_41)[name = tensor("op_8765")]; + tensor var_8766 = mul(x = var_8747, y = roti_41)[name = tensor("op_8766")]; + tensor kor_81 = sub(x = var_8765, y = var_8766)[name = tensor("kor_81")]; + tensor var_8769 = mul(x = var_8739, y = roti_41)[name = tensor("op_8769")]; + tensor var_8770 = mul(x = var_8747, y = rotr_41)[name = tensor("op_8770")]; + tensor koi_81 = add(x = var_8769, y = var_8770)[name = tensor("koi_81")]; + tensor qo_41_axis_0 = const()[name = tensor("qo_41_axis_0"), val = tensor(-1)]; + tensor qo_41 = stack(axis = qo_41_axis_0, values = (qor_81, qoi_81))[name = tensor("qo_41")]; + tensor ko_41_axis_0 = const()[name = tensor("ko_41_axis_0"), val = tensor(-1)]; + tensor ko_41 = stack(axis = ko_41_axis_0, values = (kor_81, koi_81))[name = tensor("ko_41")]; + tensor var_8799 = const()[name = tensor("op_8799"), val = tensor([1, 1, 16, 64])]; + tensor q_123 = reshape(shape = var_8799, x = qo_41)[name = tensor("q_123")]; + tensor var_8801 = const()[name = tensor("op_8801"), val = tensor([1, 1, 16, 64])]; + tensor k_83 = reshape(shape = var_8801, x = ko_41)[name = tensor("k_83")]; + tensor _inversed_8823_y_0 = const()[name = tensor("_inversed_8823_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_8823 = mul(x = ts_125, y = _inversed_8823_y_0)[name = tensor("_inversed_8823")]; + tensor var_8824 = floor(x = _inversed_8823)[name = tensor("op_8824")]; + tensor var_8825 = const()[name = tensor("op_8825"), val = tensor(0x1p+9)]; + tensor var_8826 = mul(x = var_8824, y = var_8825)[name = tensor("op_8826")]; + tensor write_indices_float_83 = sub(x = ts_125, y = var_8826)[name = tensor("write_indices_float_83")]; + tensor var_8833_dtype_0 = const()[name = tensor("op_8833_dtype_0"), val = tensor("int32")]; + tensor write_indices_41_reps_0 = const()[name = tensor("write_indices_41_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_8833 = cast(dtype = var_8833_dtype_0, x = write_indices_float_83)[name = tensor("cast_426")]; + tensor write_indices_41 = tile(reps = write_indices_41_reps_0, x = var_8833)[name = tensor("write_indices_41")]; + tensor var_8841_begin_0 = const()[name = tensor("op_8841_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8841_end_0 = const()[name = tensor("op_8841_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_8841_end_mask_0 = const()[name = tensor("op_8841_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8841_squeeze_mask_0 = const()[name = tensor("op_8841_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8841 = slice_by_index(begin = var_8841_begin_0, end = var_8841_end_0, end_mask = var_8841_end_mask_0, squeeze_mask = var_8841_squeeze_mask_0, x = cache20)[name = tensor("op_8841")]; + tensor var_8843_axis_0 = const()[name = tensor("op_8843_axis_0"), val = tensor(1)]; + tensor var_8843_mode_0 = const()[name = tensor("op_8843_mode_0"), val = tensor("update")]; + tensor var_8843_validate_indices_0 = const()[name = tensor("op_8843_validate_indices_0"), val = tensor(false)]; + tensor var_8843 = scatter_along_axis(axis = var_8843_axis_0, data = var_8841, indices = write_indices_41, mode = var_8843_mode_0, updates = k_83, validate_indices = var_8843_validate_indices_0)[name = tensor("op_8843")]; + tensor concat_141 = const()[name = tensor("concat_141"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_142 = const()[name = tensor("concat_142"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_41_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_41_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_86 = const()[name = tensor("shape_86"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_40 = const()[name = tensor("reduce_prod_40"), val = tensor(1048576)]; + tensor range_1d_40_start_0 = const()[name = tensor("range_1d_40_start_0"), val = tensor(0)]; + tensor range_1d_40_step_0 = const()[name = tensor("range_1d_40_step_0"), val = tensor(1)]; + tensor range_1d_40 = range_1d(end = reduce_prod_40, start = range_1d_40_start_0, step = range_1d_40_step_0)[name = tensor("range_1d_40")]; + tensor reshape_200 = reshape(shape = shape_86, x = range_1d_40)[name = tensor("reshape_200")]; + tensor slice_by_index_40 = slice_by_index(begin = concat_141, begin_mask = new_cache_41_internal_tensor_assign_1_begin_mask_0, end = concat_142, end_mask = new_cache_41_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_41_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_41_internal_tensor_assign_1_stride_0, x = reshape_200)[name = tensor("slice_by_index_40")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([-1])]; + tensor reshape_201 = reshape(shape = reshape_201_shape_0, x = slice_by_index_40)[name = tensor("reshape_201")]; + tensor reshape_202_shape_0 = const()[name = tensor("reshape_202_shape_0"), val = tensor([-1])]; + tensor reshape_202 = reshape(shape = reshape_202_shape_0, x = var_8843)[name = tensor("reshape_202")]; + tensor reshape_203_shape_0 = const()[name = tensor("reshape_203_shape_0"), val = tensor([-1])]; + tensor reshape_203 = reshape(shape = reshape_203_shape_0, x = cache20)[name = tensor("reshape_203")]; + tensor scatter_40_mode_0 = const()[name = tensor("scatter_40_mode_0"), val = tensor("update")]; + tensor scatter_40_axis_0 = const()[name = tensor("scatter_40_axis_0"), val = tensor(0)]; + tensor scatter_40_validate_indices_0 = const()[name = tensor("scatter_40_validate_indices_0"), val = tensor(false)]; + tensor scatter_40 = scatter(axis = scatter_40_axis_0, data = reshape_203, indices = reshape_201, mode = scatter_40_mode_0, updates = reshape_202, validate_indices = scatter_40_validate_indices_0)[name = tensor("scatter_40")]; + tensor reshape_204 = reshape(shape = shape_86, x = scatter_40)[name = tensor("reshape_204")]; + tensor var_8851_begin_0 = const()[name = tensor("op_8851_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_8851_end_0 = const()[name = tensor("op_8851_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_8851_end_mask_0 = const()[name = tensor("op_8851_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8851_squeeze_mask_0 = const()[name = tensor("op_8851_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8851 = slice_by_index(begin = var_8851_begin_0, end = var_8851_end_0, end_mask = var_8851_end_mask_0, squeeze_mask = var_8851_squeeze_mask_0, x = reshape_204)[name = tensor("op_8851")]; + tensor var_8853_axis_0 = const()[name = tensor("op_8853_axis_0"), val = tensor(1)]; + tensor var_8853_mode_0 = const()[name = tensor("op_8853_mode_0"), val = tensor("update")]; + tensor var_8853_validate_indices_0 = const()[name = tensor("op_8853_validate_indices_0"), val = tensor(false)]; + tensor var_8853 = scatter_along_axis(axis = var_8853_axis_0, data = var_8851, indices = write_indices_41, mode = var_8853_mode_0, updates = v_41, validate_indices = var_8853_validate_indices_0)[name = tensor("op_8853")]; + tensor concat_143 = const()[name = tensor("concat_143"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_144 = const()[name = tensor("concat_144"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_41_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_41_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_87 = const()[name = tensor("shape_87"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_41 = const()[name = tensor("reduce_prod_41"), val = tensor(1048576)]; + tensor range_1d_41_start_0 = const()[name = tensor("range_1d_41_start_0"), val = tensor(0)]; + tensor range_1d_41_step_0 = const()[name = tensor("range_1d_41_step_0"), val = tensor(1)]; + tensor range_1d_41 = range_1d(end = reduce_prod_41, start = range_1d_41_start_0, step = range_1d_41_step_0)[name = tensor("range_1d_41")]; + tensor reshape_205 = reshape(shape = shape_87, x = range_1d_41)[name = tensor("reshape_205")]; + tensor slice_by_index_41 = slice_by_index(begin = concat_143, begin_mask = new_cache_41_internal_tensor_assign_2_begin_mask_0, end = concat_144, end_mask = new_cache_41_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_41_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_41_internal_tensor_assign_2_stride_0, x = reshape_205)[name = tensor("slice_by_index_41")]; + tensor reshape_206_shape_0 = const()[name = tensor("reshape_206_shape_0"), val = tensor([-1])]; + tensor reshape_206 = reshape(shape = reshape_206_shape_0, x = slice_by_index_41)[name = tensor("reshape_206")]; + tensor reshape_207_shape_0 = const()[name = tensor("reshape_207_shape_0"), val = tensor([-1])]; + tensor reshape_207 = reshape(shape = reshape_207_shape_0, x = var_8853)[name = tensor("reshape_207")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([-1])]; + tensor reshape_208 = reshape(shape = reshape_208_shape_0, x = reshape_204)[name = tensor("reshape_208")]; + tensor scatter_41_mode_0 = const()[name = tensor("scatter_41_mode_0"), val = tensor("update")]; + tensor scatter_41_axis_0 = const()[name = tensor("scatter_41_axis_0"), val = tensor(0)]; + tensor scatter_41_validate_indices_0 = const()[name = tensor("scatter_41_validate_indices_0"), val = tensor(false)]; + tensor scatter_41 = scatter(axis = scatter_41_axis_0, data = reshape_208, indices = reshape_206, mode = scatter_41_mode_0, updates = reshape_207, validate_indices = scatter_41_validate_indices_0)[name = tensor("scatter_41")]; + tensor new_cache_41_internal_tensor_assign_2 = reshape(shape = shape_87, x = scatter_41)[name = tensor("reshape_209")]; + tensor keys_121_begin_0 = const()[name = tensor("keys_121_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_121_end_0 = const()[name = tensor("keys_121_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_121_end_mask_0 = const()[name = tensor("keys_121_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_121_squeeze_mask_0 = const()[name = tensor("keys_121_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_121 = slice_by_index(begin = keys_121_begin_0, end = keys_121_end_0, end_mask = keys_121_end_mask_0, squeeze_mask = keys_121_squeeze_mask_0, x = new_cache_41_internal_tensor_assign_2)[name = tensor("keys_121")]; + tensor values_121_begin_0 = const()[name = tensor("values_121_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_121_end_0 = const()[name = tensor("values_121_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_121_end_mask_0 = const()[name = tensor("values_121_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_121_squeeze_mask_0 = const()[name = tensor("values_121_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_121 = slice_by_index(begin = values_121_begin_0, end = values_121_end_0, end_mask = values_121_end_mask_0, squeeze_mask = values_121_squeeze_mask_0, x = new_cache_41_internal_tensor_assign_2)[name = tensor("values_121")]; + tensor var_8865 = not_equal(x = keys_121, y = keys_121)[name = tensor("op_8865")]; + tensor keys_123 = select(a = var_491, b = keys_121, cond = var_8865)[name = tensor("keys_123")]; + tensor var_8873 = not_equal(x = values_121, y = values_121)[name = tensor("op_8873")]; + tensor values_123 = select(a = var_491, b = values_121, cond = var_8873)[name = tensor("values_123")]; + tensor var_8897 = const()[name = tensor("op_8897"), val = tensor([0, 2, 1, 3])]; + tensor var_8910 = const()[name = tensor("op_8910"), val = tensor([1, 1, 1])]; + tensor var_8911 = reshape(shape = var_8910, x = position20)[name = tensor("op_8911")]; + tensor var_8928 = const()[name = tensor("op_8928"), val = tensor(0x1p+0)]; + tensor valid_len_41 = add(x = var_8911, y = var_8928)[name = tensor("valid_len_41")]; + tensor valid_mask_41 = less(x = k_positions_1_promoted, y = valid_len_41)[name = tensor("valid_mask_41")]; + tensor causal_mask_41 = less_equal(x = k_positions_1_promoted, y = var_8911)[name = tensor("causal_mask_41")]; + tensor attn_mask_81 = logical_and(x = valid_mask_41, y = causal_mask_41)[name = tensor("attn_mask_81")]; + tensor attn_mask_83_axes_0 = const()[name = tensor("attn_mask_83_axes_0"), val = tensor([1])]; + tensor attn_mask_83 = expand_dims(axes = attn_mask_83_axes_0, x = attn_mask_81)[name = tensor("attn_mask_83")]; + tensor var_8940 = const()[name = tensor("op_8940"), val = tensor([0x1.fffe5cp-4])]; + tensor var_8946_transpose_x_0 = const()[name = tensor("op_8946_transpose_x_0"), val = tensor(false)]; + tensor var_8946_transpose_y_0 = const()[name = tensor("op_8946_transpose_y_0"), val = tensor(false)]; + tensor transpose_109_perm_0 = const()[name = tensor("transpose_109_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_110_perm_0 = const()[name = tensor("transpose_110_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = keys_123)[name = tensor("transpose_124")]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = q_123)[name = tensor("transpose_125")]; + tensor var_8946 = matmul(transpose_x = var_8946_transpose_x_0, transpose_y = var_8946_transpose_y_0, x = transpose_109, y = transpose_110)[name = tensor("op_8946")]; + tensor attn_weights_121 = mul(x = var_8946, y = var_8940)[name = tensor("attn_weights_121")]; + tensor var_8948 = logical_not(x = attn_mask_83)[name = tensor("op_8948")]; + tensor var_8949 = const()[name = tensor("op_8949"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_123 = select(a = var_8949, b = attn_weights_121, cond = var_8948)[name = tensor("attn_weights_123")]; + tensor var_8951 = const()[name = tensor("op_8951"), val = tensor(-1)]; + tensor attn_weights_125 = softmax(axis = var_8951, x = attn_weights_123)[name = tensor("attn_weights_125")]; + tensor attn_output_41_transpose_x_0 = const()[name = tensor("attn_output_41_transpose_x_0"), val = tensor(false)]; + tensor attn_output_41_transpose_y_0 = const()[name = tensor("attn_output_41_transpose_y_0"), val = tensor(false)]; + tensor values_125 = transpose(perm = var_8897, x = values_123)[name = tensor("transpose_126")]; + tensor attn_output_41 = matmul(transpose_x = attn_output_41_transpose_x_0, transpose_y = attn_output_41_transpose_y_0, x = attn_weights_125, y = values_125)[name = tensor("attn_output_41")]; + tensor var_8959 = const()[name = tensor("op_8959"), val = tensor([0, 2, 1, 3])]; + tensor var_8962 = const()[name = tensor("op_8962"), val = tensor([1, 1, 1024])]; + tensor var_8960 = transpose(perm = var_8959, x = attn_output_41)[name = tensor("transpose_123")]; + tensor input_203 = reshape(shape = var_8962, x = var_8960)[name = tensor("input_203")]; + tensor attn_out_41 = linear(bias = linear_1_bias_0, weight = attn20_out_proj_weight, x = input_203)[name = tensor("linear_81")]; + tensor var_8968 = const()[name = tensor("op_8968"), val = tensor(0x1p+0)]; + tensor var_8969 = add(x = position20, y = var_8968)[name = tensor("op_8969")]; + tensor input_205 = add(x = input_201, y = attn_out_41)[name = tensor("input_205")]; + tensor var_8973 = const()[name = tensor("op_8973"), val = tensor(0x1.4f8b58p-17)]; + tensor input_207_axes_0 = const()[name = tensor("input_207_axes_0"), val = tensor([-1])]; + tensor input_207 = layer_norm(axes = input_207_axes_0, beta = norm20_2_bias, epsilon = var_8973, gamma = norm20_2_weight, x = input_205)[name = tensor("input_207")]; + tensor var_8981 = linear(bias = linear_2_bias_0, weight = linear20_1_weight, x = input_207)[name = tensor("linear_82")]; + tensor input_209_mode_0 = const()[name = tensor("input_209_mode_0"), val = tensor("EXACT")]; + tensor input_209 = gelu(mode = input_209_mode_0, x = var_8981)[name = tensor("input_209")]; + tensor ffn_out_41 = linear(bias = linear_1_bias_0, weight = linear20_2_weight, x = input_209)[name = tensor("linear_83")]; + tensor input_211 = add(x = input_205, y = ffn_out_41)[name = tensor("input_211")]; + tensor var_8990 = const()[name = tensor("op_8990"), val = tensor(0x1.4f8b58p-17)]; + tensor x_43_axes_0 = const()[name = tensor("x_43_axes_0"), val = tensor([-1])]; + tensor x_43 = layer_norm(axes = x_43_axes_0, beta = norm21_1_bias, epsilon = var_8990, gamma = norm21_1_weight, x = input_211)[name = tensor("x_43")]; + tensor var_9022 = linear(bias = linear_0_bias_0, weight = attn21_in_proj_weight, x = x_43)[name = tensor("linear_84")]; + tensor var_9026 = const()[name = tensor("op_9026"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_43 = reshape(shape = var_9026, x = var_9022)[name = tensor("qkv_43")]; + tensor q_127_begin_0 = const()[name = tensor("q_127_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_127_end_0 = const()[name = tensor("q_127_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_127_end_mask_0 = const()[name = tensor("q_127_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_127_squeeze_mask_0 = const()[name = tensor("q_127_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_127 = slice_by_index(begin = q_127_begin_0, end = q_127_end_0, end_mask = q_127_end_mask_0, squeeze_mask = q_127_squeeze_mask_0, x = qkv_43)[name = tensor("q_127")]; + tensor k_85_begin_0 = const()[name = tensor("k_85_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_85_end_0 = const()[name = tensor("k_85_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_85_end_mask_0 = const()[name = tensor("k_85_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_85_squeeze_mask_0 = const()[name = tensor("k_85_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_85 = slice_by_index(begin = k_85_begin_0, end = k_85_end_0, end_mask = k_85_end_mask_0, squeeze_mask = k_85_squeeze_mask_0, x = qkv_43)[name = tensor("k_85")]; + tensor v_43_begin_0 = const()[name = tensor("v_43_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_43_end_0 = const()[name = tensor("v_43_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_43_end_mask_0 = const()[name = tensor("v_43_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_43_squeeze_mask_0 = const()[name = tensor("v_43_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_43 = slice_by_index(begin = v_43_begin_0, end = v_43_end_0, end_mask = v_43_end_mask_0, squeeze_mask = v_43_squeeze_mask_0, x = qkv_43)[name = tensor("v_43")]; + tensor freqs_43 = const()[name = tensor("freqs_43"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172744192)))]; + tensor var_9130 = const()[name = tensor("op_9130"), val = tensor([1, 1, 1, 1])]; + tensor ts_131 = reshape(shape = var_9130, x = position21)[name = tensor("ts_131")]; + tensor var_9134 = const()[name = tensor("op_9134"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_43 = reshape(shape = var_9134, x = q_127)[name = tensor("q_complex_43")]; + tensor var_9138 = const()[name = tensor("op_9138"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_43 = reshape(shape = var_9138, x = k_85)[name = tensor("k_complex_43")]; + tensor var_9142_begin_0 = const()[name = tensor("op_9142_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9142_end_0 = const()[name = tensor("op_9142_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9142_end_mask_0 = const()[name = tensor("op_9142_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9142_squeeze_mask_0 = const()[name = tensor("op_9142_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9142 = slice_by_index(begin = var_9142_begin_0, end = var_9142_end_0, end_mask = var_9142_end_mask_0, squeeze_mask = var_9142_squeeze_mask_0, x = q_complex_43)[name = tensor("op_9142")]; + tensor var_9150_begin_0 = const()[name = tensor("op_9150_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9150_end_0 = const()[name = tensor("op_9150_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9150_end_mask_0 = const()[name = tensor("op_9150_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9150_squeeze_mask_0 = const()[name = tensor("op_9150_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9150 = slice_by_index(begin = var_9150_begin_0, end = var_9150_end_0, end_mask = var_9150_end_mask_0, squeeze_mask = var_9150_squeeze_mask_0, x = q_complex_43)[name = tensor("op_9150")]; + tensor var_9158_begin_0 = const()[name = tensor("op_9158_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9158_end_0 = const()[name = tensor("op_9158_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9158_end_mask_0 = const()[name = tensor("op_9158_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9158_squeeze_mask_0 = const()[name = tensor("op_9158_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9158 = slice_by_index(begin = var_9158_begin_0, end = var_9158_end_0, end_mask = var_9158_end_mask_0, squeeze_mask = var_9158_squeeze_mask_0, x = k_complex_43)[name = tensor("op_9158")]; + tensor var_9166_begin_0 = const()[name = tensor("op_9166_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9166_end_0 = const()[name = tensor("op_9166_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9166_end_mask_0 = const()[name = tensor("op_9166_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9166_squeeze_mask_0 = const()[name = tensor("op_9166_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9166 = slice_by_index(begin = var_9166_begin_0, end = var_9166_end_0, end_mask = var_9166_end_mask_0, squeeze_mask = var_9166_squeeze_mask_0, x = k_complex_43)[name = tensor("op_9166")]; + tensor var_9172 = mul(x = freqs_43, y = ts_131)[name = tensor("op_9172")]; + tensor rotr_43 = cos(x = var_9172)[name = tensor("rotr_43")]; + tensor roti_43 = sin(x = var_9172)[name = tensor("roti_43")]; + tensor var_9176 = mul(x = var_9142, y = rotr_43)[name = tensor("op_9176")]; + tensor var_9177 = mul(x = var_9150, y = roti_43)[name = tensor("op_9177")]; + tensor qor_85 = sub(x = var_9176, y = var_9177)[name = tensor("qor_85")]; + tensor var_9180 = mul(x = var_9142, y = roti_43)[name = tensor("op_9180")]; + tensor var_9181 = mul(x = var_9150, y = rotr_43)[name = tensor("op_9181")]; + tensor qoi_85 = add(x = var_9180, y = var_9181)[name = tensor("qoi_85")]; + tensor var_9184 = mul(x = var_9158, y = rotr_43)[name = tensor("op_9184")]; + tensor var_9185 = mul(x = var_9166, y = roti_43)[name = tensor("op_9185")]; + tensor kor_85 = sub(x = var_9184, y = var_9185)[name = tensor("kor_85")]; + tensor var_9188 = mul(x = var_9158, y = roti_43)[name = tensor("op_9188")]; + tensor var_9189 = mul(x = var_9166, y = rotr_43)[name = tensor("op_9189")]; + tensor koi_85 = add(x = var_9188, y = var_9189)[name = tensor("koi_85")]; + tensor qo_43_axis_0 = const()[name = tensor("qo_43_axis_0"), val = tensor(-1)]; + tensor qo_43 = stack(axis = qo_43_axis_0, values = (qor_85, qoi_85))[name = tensor("qo_43")]; + tensor ko_43_axis_0 = const()[name = tensor("ko_43_axis_0"), val = tensor(-1)]; + tensor ko_43 = stack(axis = ko_43_axis_0, values = (kor_85, koi_85))[name = tensor("ko_43")]; + tensor var_9218 = const()[name = tensor("op_9218"), val = tensor([1, 1, 16, 64])]; + tensor q_129 = reshape(shape = var_9218, x = qo_43)[name = tensor("q_129")]; + tensor var_9220 = const()[name = tensor("op_9220"), val = tensor([1, 1, 16, 64])]; + tensor k_87 = reshape(shape = var_9220, x = ko_43)[name = tensor("k_87")]; + tensor _inversed_9242_y_0 = const()[name = tensor("_inversed_9242_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_9242 = mul(x = ts_131, y = _inversed_9242_y_0)[name = tensor("_inversed_9242")]; + tensor var_9243 = floor(x = _inversed_9242)[name = tensor("op_9243")]; + tensor var_9244 = const()[name = tensor("op_9244"), val = tensor(0x1p+9)]; + tensor var_9245 = mul(x = var_9243, y = var_9244)[name = tensor("op_9245")]; + tensor write_indices_float_87 = sub(x = ts_131, y = var_9245)[name = tensor("write_indices_float_87")]; + tensor var_9252_dtype_0 = const()[name = tensor("op_9252_dtype_0"), val = tensor("int32")]; + tensor write_indices_43_reps_0 = const()[name = tensor("write_indices_43_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_9252 = cast(dtype = var_9252_dtype_0, x = write_indices_float_87)[name = tensor("cast_425")]; + tensor write_indices_43 = tile(reps = write_indices_43_reps_0, x = var_9252)[name = tensor("write_indices_43")]; + tensor var_9260_begin_0 = const()[name = tensor("op_9260_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9260_end_0 = const()[name = tensor("op_9260_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_9260_end_mask_0 = const()[name = tensor("op_9260_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9260_squeeze_mask_0 = const()[name = tensor("op_9260_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9260 = slice_by_index(begin = var_9260_begin_0, end = var_9260_end_0, end_mask = var_9260_end_mask_0, squeeze_mask = var_9260_squeeze_mask_0, x = cache21)[name = tensor("op_9260")]; + tensor var_9262_axis_0 = const()[name = tensor("op_9262_axis_0"), val = tensor(1)]; + tensor var_9262_mode_0 = const()[name = tensor("op_9262_mode_0"), val = tensor("update")]; + tensor var_9262_validate_indices_0 = const()[name = tensor("op_9262_validate_indices_0"), val = tensor(false)]; + tensor var_9262 = scatter_along_axis(axis = var_9262_axis_0, data = var_9260, indices = write_indices_43, mode = var_9262_mode_0, updates = k_87, validate_indices = var_9262_validate_indices_0)[name = tensor("op_9262")]; + tensor concat_148 = const()[name = tensor("concat_148"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_149 = const()[name = tensor("concat_149"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_43_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_43_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_88 = const()[name = tensor("shape_88"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_42 = const()[name = tensor("reduce_prod_42"), val = tensor(1048576)]; + tensor range_1d_42_start_0 = const()[name = tensor("range_1d_42_start_0"), val = tensor(0)]; + tensor range_1d_42_step_0 = const()[name = tensor("range_1d_42_step_0"), val = tensor(1)]; + tensor range_1d_42 = range_1d(end = reduce_prod_42, start = range_1d_42_start_0, step = range_1d_42_step_0)[name = tensor("range_1d_42")]; + tensor reshape_210 = reshape(shape = shape_88, x = range_1d_42)[name = tensor("reshape_210")]; + tensor slice_by_index_42 = slice_by_index(begin = concat_148, begin_mask = new_cache_43_internal_tensor_assign_1_begin_mask_0, end = concat_149, end_mask = new_cache_43_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_43_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_43_internal_tensor_assign_1_stride_0, x = reshape_210)[name = tensor("slice_by_index_42")]; + tensor reshape_211_shape_0 = const()[name = tensor("reshape_211_shape_0"), val = tensor([-1])]; + tensor reshape_211 = reshape(shape = reshape_211_shape_0, x = slice_by_index_42)[name = tensor("reshape_211")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([-1])]; + tensor reshape_212 = reshape(shape = reshape_212_shape_0, x = var_9262)[name = tensor("reshape_212")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([-1])]; + tensor reshape_213 = reshape(shape = reshape_213_shape_0, x = cache21)[name = tensor("reshape_213")]; + tensor scatter_42_mode_0 = const()[name = tensor("scatter_42_mode_0"), val = tensor("update")]; + tensor scatter_42_axis_0 = const()[name = tensor("scatter_42_axis_0"), val = tensor(0)]; + tensor scatter_42_validate_indices_0 = const()[name = tensor("scatter_42_validate_indices_0"), val = tensor(false)]; + tensor scatter_42 = scatter(axis = scatter_42_axis_0, data = reshape_213, indices = reshape_211, mode = scatter_42_mode_0, updates = reshape_212, validate_indices = scatter_42_validate_indices_0)[name = tensor("scatter_42")]; + tensor reshape_214 = reshape(shape = shape_88, x = scatter_42)[name = tensor("reshape_214")]; + tensor var_9270_begin_0 = const()[name = tensor("op_9270_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_9270_end_0 = const()[name = tensor("op_9270_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_9270_end_mask_0 = const()[name = tensor("op_9270_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9270_squeeze_mask_0 = const()[name = tensor("op_9270_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9270 = slice_by_index(begin = var_9270_begin_0, end = var_9270_end_0, end_mask = var_9270_end_mask_0, squeeze_mask = var_9270_squeeze_mask_0, x = reshape_214)[name = tensor("op_9270")]; + tensor var_9272_axis_0 = const()[name = tensor("op_9272_axis_0"), val = tensor(1)]; + tensor var_9272_mode_0 = const()[name = tensor("op_9272_mode_0"), val = tensor("update")]; + tensor var_9272_validate_indices_0 = const()[name = tensor("op_9272_validate_indices_0"), val = tensor(false)]; + tensor var_9272 = scatter_along_axis(axis = var_9272_axis_0, data = var_9270, indices = write_indices_43, mode = var_9272_mode_0, updates = v_43, validate_indices = var_9272_validate_indices_0)[name = tensor("op_9272")]; + tensor concat_150 = const()[name = tensor("concat_150"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_151 = const()[name = tensor("concat_151"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_43_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_43_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_89 = const()[name = tensor("shape_89"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_43 = const()[name = tensor("reduce_prod_43"), val = tensor(1048576)]; + tensor range_1d_43_start_0 = const()[name = tensor("range_1d_43_start_0"), val = tensor(0)]; + tensor range_1d_43_step_0 = const()[name = tensor("range_1d_43_step_0"), val = tensor(1)]; + tensor range_1d_43 = range_1d(end = reduce_prod_43, start = range_1d_43_start_0, step = range_1d_43_step_0)[name = tensor("range_1d_43")]; + tensor reshape_215 = reshape(shape = shape_89, x = range_1d_43)[name = tensor("reshape_215")]; + tensor slice_by_index_43 = slice_by_index(begin = concat_150, begin_mask = new_cache_43_internal_tensor_assign_2_begin_mask_0, end = concat_151, end_mask = new_cache_43_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_43_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_43_internal_tensor_assign_2_stride_0, x = reshape_215)[name = tensor("slice_by_index_43")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([-1])]; + tensor reshape_216 = reshape(shape = reshape_216_shape_0, x = slice_by_index_43)[name = tensor("reshape_216")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([-1])]; + tensor reshape_217 = reshape(shape = reshape_217_shape_0, x = var_9272)[name = tensor("reshape_217")]; + tensor reshape_218_shape_0 = const()[name = tensor("reshape_218_shape_0"), val = tensor([-1])]; + tensor reshape_218 = reshape(shape = reshape_218_shape_0, x = reshape_214)[name = tensor("reshape_218")]; + tensor scatter_43_mode_0 = const()[name = tensor("scatter_43_mode_0"), val = tensor("update")]; + tensor scatter_43_axis_0 = const()[name = tensor("scatter_43_axis_0"), val = tensor(0)]; + tensor scatter_43_validate_indices_0 = const()[name = tensor("scatter_43_validate_indices_0"), val = tensor(false)]; + tensor scatter_43 = scatter(axis = scatter_43_axis_0, data = reshape_218, indices = reshape_216, mode = scatter_43_mode_0, updates = reshape_217, validate_indices = scatter_43_validate_indices_0)[name = tensor("scatter_43")]; + tensor new_cache_43_internal_tensor_assign_2 = reshape(shape = shape_89, x = scatter_43)[name = tensor("reshape_219")]; + tensor keys_127_begin_0 = const()[name = tensor("keys_127_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_127_end_0 = const()[name = tensor("keys_127_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_127_end_mask_0 = const()[name = tensor("keys_127_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_127_squeeze_mask_0 = const()[name = tensor("keys_127_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_127 = slice_by_index(begin = keys_127_begin_0, end = keys_127_end_0, end_mask = keys_127_end_mask_0, squeeze_mask = keys_127_squeeze_mask_0, x = new_cache_43_internal_tensor_assign_2)[name = tensor("keys_127")]; + tensor values_127_begin_0 = const()[name = tensor("values_127_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_127_end_0 = const()[name = tensor("values_127_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_127_end_mask_0 = const()[name = tensor("values_127_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_127_squeeze_mask_0 = const()[name = tensor("values_127_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_127 = slice_by_index(begin = values_127_begin_0, end = values_127_end_0, end_mask = values_127_end_mask_0, squeeze_mask = values_127_squeeze_mask_0, x = new_cache_43_internal_tensor_assign_2)[name = tensor("values_127")]; + tensor var_9284 = not_equal(x = keys_127, y = keys_127)[name = tensor("op_9284")]; + tensor keys_129 = select(a = var_491, b = keys_127, cond = var_9284)[name = tensor("keys_129")]; + tensor var_9292 = not_equal(x = values_127, y = values_127)[name = tensor("op_9292")]; + tensor values_129 = select(a = var_491, b = values_127, cond = var_9292)[name = tensor("values_129")]; + tensor var_9316 = const()[name = tensor("op_9316"), val = tensor([0, 2, 1, 3])]; + tensor var_9329 = const()[name = tensor("op_9329"), val = tensor([1, 1, 1])]; + tensor var_9330 = reshape(shape = var_9329, x = position21)[name = tensor("op_9330")]; + tensor var_9347 = const()[name = tensor("op_9347"), val = tensor(0x1p+0)]; + tensor valid_len_43 = add(x = var_9330, y = var_9347)[name = tensor("valid_len_43")]; + tensor valid_mask_43 = less(x = k_positions_1_promoted, y = valid_len_43)[name = tensor("valid_mask_43")]; + tensor causal_mask_43 = less_equal(x = k_positions_1_promoted, y = var_9330)[name = tensor("causal_mask_43")]; + tensor attn_mask_85 = logical_and(x = valid_mask_43, y = causal_mask_43)[name = tensor("attn_mask_85")]; + tensor attn_mask_87_axes_0 = const()[name = tensor("attn_mask_87_axes_0"), val = tensor([1])]; + tensor attn_mask_87 = expand_dims(axes = attn_mask_87_axes_0, x = attn_mask_85)[name = tensor("attn_mask_87")]; + tensor var_9359 = const()[name = tensor("op_9359"), val = tensor([0x1.fffe5cp-4])]; + tensor var_9365_transpose_x_0 = const()[name = tensor("op_9365_transpose_x_0"), val = tensor(false)]; + tensor var_9365_transpose_y_0 = const()[name = tensor("op_9365_transpose_y_0"), val = tensor(false)]; + tensor transpose_111_perm_0 = const()[name = tensor("transpose_111_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_112_perm_0 = const()[name = tensor("transpose_112_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = keys_129)[name = tensor("transpose_120")]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = q_129)[name = tensor("transpose_121")]; + tensor var_9365 = matmul(transpose_x = var_9365_transpose_x_0, transpose_y = var_9365_transpose_y_0, x = transpose_111, y = transpose_112)[name = tensor("op_9365")]; + tensor attn_weights_127 = mul(x = var_9365, y = var_9359)[name = tensor("attn_weights_127")]; + tensor var_9367 = logical_not(x = attn_mask_87)[name = tensor("op_9367")]; + tensor var_9368 = const()[name = tensor("op_9368"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_129 = select(a = var_9368, b = attn_weights_127, cond = var_9367)[name = tensor("attn_weights_129")]; + tensor var_9370 = const()[name = tensor("op_9370"), val = tensor(-1)]; + tensor attn_weights_131 = softmax(axis = var_9370, x = attn_weights_129)[name = tensor("attn_weights_131")]; + tensor attn_output_43_transpose_x_0 = const()[name = tensor("attn_output_43_transpose_x_0"), val = tensor(false)]; + tensor attn_output_43_transpose_y_0 = const()[name = tensor("attn_output_43_transpose_y_0"), val = tensor(false)]; + tensor values_131 = transpose(perm = var_9316, x = values_129)[name = tensor("transpose_122")]; + tensor attn_output_43 = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = attn_weights_131, y = values_131)[name = tensor("attn_output_43")]; + tensor var_9378 = const()[name = tensor("op_9378"), val = tensor([0, 2, 1, 3])]; + tensor var_9381 = const()[name = tensor("op_9381"), val = tensor([1, 1, 1024])]; + tensor var_9379 = transpose(perm = var_9378, x = attn_output_43)[name = tensor("transpose_119")]; + tensor input_213 = reshape(shape = var_9381, x = var_9379)[name = tensor("input_213")]; + tensor attn_out_43 = linear(bias = linear_1_bias_0, weight = attn21_out_proj_weight, x = input_213)[name = tensor("linear_85")]; + tensor var_9387 = const()[name = tensor("op_9387"), val = tensor(0x1p+0)]; + tensor var_9388 = add(x = position21, y = var_9387)[name = tensor("op_9388")]; + tensor input_215 = add(x = input_211, y = attn_out_43)[name = tensor("input_215")]; + tensor var_9392 = const()[name = tensor("op_9392"), val = tensor(0x1.4f8b58p-17)]; + tensor input_217_axes_0 = const()[name = tensor("input_217_axes_0"), val = tensor([-1])]; + tensor input_217 = layer_norm(axes = input_217_axes_0, beta = norm21_2_bias, epsilon = var_9392, gamma = norm21_2_weight, x = input_215)[name = tensor("input_217")]; + tensor var_9400 = linear(bias = linear_2_bias_0, weight = linear21_1_weight, x = input_217)[name = tensor("linear_86")]; + tensor input_219_mode_0 = const()[name = tensor("input_219_mode_0"), val = tensor("EXACT")]; + tensor input_219 = gelu(mode = input_219_mode_0, x = var_9400)[name = tensor("input_219")]; + tensor ffn_out_43 = linear(bias = linear_1_bias_0, weight = linear21_2_weight, x = input_219)[name = tensor("linear_87")]; + tensor input_221 = add(x = input_215, y = ffn_out_43)[name = tensor("input_221")]; + tensor var_9409 = const()[name = tensor("op_9409"), val = tensor(0x1.4f8b58p-17)]; + tensor x_45_axes_0 = const()[name = tensor("x_45_axes_0"), val = tensor([-1])]; + tensor x_45 = layer_norm(axes = x_45_axes_0, beta = norm22_1_bias, epsilon = var_9409, gamma = norm22_1_weight, x = input_221)[name = tensor("x_45")]; + tensor var_9441 = linear(bias = linear_0_bias_0, weight = attn22_in_proj_weight, x = x_45)[name = tensor("linear_88")]; + tensor var_9445 = const()[name = tensor("op_9445"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_45 = reshape(shape = var_9445, x = var_9441)[name = tensor("qkv_45")]; + tensor q_133_begin_0 = const()[name = tensor("q_133_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_133_end_0 = const()[name = tensor("q_133_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_133_end_mask_0 = const()[name = tensor("q_133_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_133_squeeze_mask_0 = const()[name = tensor("q_133_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_133 = slice_by_index(begin = q_133_begin_0, end = q_133_end_0, end_mask = q_133_end_mask_0, squeeze_mask = q_133_squeeze_mask_0, x = qkv_45)[name = tensor("q_133")]; + tensor k_89_begin_0 = const()[name = tensor("k_89_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_89_end_0 = const()[name = tensor("k_89_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_89_end_mask_0 = const()[name = tensor("k_89_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_89_squeeze_mask_0 = const()[name = tensor("k_89_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_89 = slice_by_index(begin = k_89_begin_0, end = k_89_end_0, end_mask = k_89_end_mask_0, squeeze_mask = k_89_squeeze_mask_0, x = qkv_45)[name = tensor("k_89")]; + tensor v_45_begin_0 = const()[name = tensor("v_45_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_45_end_0 = const()[name = tensor("v_45_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_45_end_mask_0 = const()[name = tensor("v_45_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_45_squeeze_mask_0 = const()[name = tensor("v_45_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_45 = slice_by_index(begin = v_45_begin_0, end = v_45_end_0, end_mask = v_45_end_mask_0, squeeze_mask = v_45_squeeze_mask_0, x = qkv_45)[name = tensor("v_45")]; + tensor freqs_45 = const()[name = tensor("freqs_45"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172744384)))]; + tensor var_9549 = const()[name = tensor("op_9549"), val = tensor([1, 1, 1, 1])]; + tensor ts_137 = reshape(shape = var_9549, x = position22)[name = tensor("ts_137")]; + tensor var_9553 = const()[name = tensor("op_9553"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_45 = reshape(shape = var_9553, x = q_133)[name = tensor("q_complex_45")]; + tensor var_9557 = const()[name = tensor("op_9557"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_45 = reshape(shape = var_9557, x = k_89)[name = tensor("k_complex_45")]; + tensor var_9561_begin_0 = const()[name = tensor("op_9561_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9561_end_0 = const()[name = tensor("op_9561_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9561_end_mask_0 = const()[name = tensor("op_9561_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9561_squeeze_mask_0 = const()[name = tensor("op_9561_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9561 = slice_by_index(begin = var_9561_begin_0, end = var_9561_end_0, end_mask = var_9561_end_mask_0, squeeze_mask = var_9561_squeeze_mask_0, x = q_complex_45)[name = tensor("op_9561")]; + tensor var_9569_begin_0 = const()[name = tensor("op_9569_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9569_end_0 = const()[name = tensor("op_9569_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9569_end_mask_0 = const()[name = tensor("op_9569_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9569_squeeze_mask_0 = const()[name = tensor("op_9569_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9569 = slice_by_index(begin = var_9569_begin_0, end = var_9569_end_0, end_mask = var_9569_end_mask_0, squeeze_mask = var_9569_squeeze_mask_0, x = q_complex_45)[name = tensor("op_9569")]; + tensor var_9577_begin_0 = const()[name = tensor("op_9577_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9577_end_0 = const()[name = tensor("op_9577_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9577_end_mask_0 = const()[name = tensor("op_9577_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9577_squeeze_mask_0 = const()[name = tensor("op_9577_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9577 = slice_by_index(begin = var_9577_begin_0, end = var_9577_end_0, end_mask = var_9577_end_mask_0, squeeze_mask = var_9577_squeeze_mask_0, x = k_complex_45)[name = tensor("op_9577")]; + tensor var_9585_begin_0 = const()[name = tensor("op_9585_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9585_end_0 = const()[name = tensor("op_9585_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9585_end_mask_0 = const()[name = tensor("op_9585_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9585_squeeze_mask_0 = const()[name = tensor("op_9585_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9585 = slice_by_index(begin = var_9585_begin_0, end = var_9585_end_0, end_mask = var_9585_end_mask_0, squeeze_mask = var_9585_squeeze_mask_0, x = k_complex_45)[name = tensor("op_9585")]; + tensor var_9591 = mul(x = freqs_45, y = ts_137)[name = tensor("op_9591")]; + tensor rotr_45 = cos(x = var_9591)[name = tensor("rotr_45")]; + tensor roti_45 = sin(x = var_9591)[name = tensor("roti_45")]; + tensor var_9595 = mul(x = var_9561, y = rotr_45)[name = tensor("op_9595")]; + tensor var_9596 = mul(x = var_9569, y = roti_45)[name = tensor("op_9596")]; + tensor qor_89 = sub(x = var_9595, y = var_9596)[name = tensor("qor_89")]; + tensor var_9599 = mul(x = var_9561, y = roti_45)[name = tensor("op_9599")]; + tensor var_9600 = mul(x = var_9569, y = rotr_45)[name = tensor("op_9600")]; + tensor qoi_89 = add(x = var_9599, y = var_9600)[name = tensor("qoi_89")]; + tensor var_9603 = mul(x = var_9577, y = rotr_45)[name = tensor("op_9603")]; + tensor var_9604 = mul(x = var_9585, y = roti_45)[name = tensor("op_9604")]; + tensor kor_89 = sub(x = var_9603, y = var_9604)[name = tensor("kor_89")]; + tensor var_9607 = mul(x = var_9577, y = roti_45)[name = tensor("op_9607")]; + tensor var_9608 = mul(x = var_9585, y = rotr_45)[name = tensor("op_9608")]; + tensor koi_89 = add(x = var_9607, y = var_9608)[name = tensor("koi_89")]; + tensor qo_45_axis_0 = const()[name = tensor("qo_45_axis_0"), val = tensor(-1)]; + tensor qo_45 = stack(axis = qo_45_axis_0, values = (qor_89, qoi_89))[name = tensor("qo_45")]; + tensor ko_45_axis_0 = const()[name = tensor("ko_45_axis_0"), val = tensor(-1)]; + tensor ko_45 = stack(axis = ko_45_axis_0, values = (kor_89, koi_89))[name = tensor("ko_45")]; + tensor var_9637 = const()[name = tensor("op_9637"), val = tensor([1, 1, 16, 64])]; + tensor q_135 = reshape(shape = var_9637, x = qo_45)[name = tensor("q_135")]; + tensor var_9639 = const()[name = tensor("op_9639"), val = tensor([1, 1, 16, 64])]; + tensor k_91 = reshape(shape = var_9639, x = ko_45)[name = tensor("k_91")]; + tensor _inversed_9661_y_0 = const()[name = tensor("_inversed_9661_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_9661 = mul(x = ts_137, y = _inversed_9661_y_0)[name = tensor("_inversed_9661")]; + tensor var_9662 = floor(x = _inversed_9661)[name = tensor("op_9662")]; + tensor var_9663 = const()[name = tensor("op_9663"), val = tensor(0x1p+9)]; + tensor var_9664 = mul(x = var_9662, y = var_9663)[name = tensor("op_9664")]; + tensor write_indices_float_91 = sub(x = ts_137, y = var_9664)[name = tensor("write_indices_float_91")]; + tensor var_9671_dtype_0 = const()[name = tensor("op_9671_dtype_0"), val = tensor("int32")]; + tensor write_indices_45_reps_0 = const()[name = tensor("write_indices_45_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_9671 = cast(dtype = var_9671_dtype_0, x = write_indices_float_91)[name = tensor("cast_424")]; + tensor write_indices_45 = tile(reps = write_indices_45_reps_0, x = var_9671)[name = tensor("write_indices_45")]; + tensor var_9679_begin_0 = const()[name = tensor("op_9679_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9679_end_0 = const()[name = tensor("op_9679_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_9679_end_mask_0 = const()[name = tensor("op_9679_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9679_squeeze_mask_0 = const()[name = tensor("op_9679_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9679 = slice_by_index(begin = var_9679_begin_0, end = var_9679_end_0, end_mask = var_9679_end_mask_0, squeeze_mask = var_9679_squeeze_mask_0, x = cache22)[name = tensor("op_9679")]; + tensor var_9681_axis_0 = const()[name = tensor("op_9681_axis_0"), val = tensor(1)]; + tensor var_9681_mode_0 = const()[name = tensor("op_9681_mode_0"), val = tensor("update")]; + tensor var_9681_validate_indices_0 = const()[name = tensor("op_9681_validate_indices_0"), val = tensor(false)]; + tensor var_9681 = scatter_along_axis(axis = var_9681_axis_0, data = var_9679, indices = write_indices_45, mode = var_9681_mode_0, updates = k_91, validate_indices = var_9681_validate_indices_0)[name = tensor("op_9681")]; + tensor concat_155 = const()[name = tensor("concat_155"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_156 = const()[name = tensor("concat_156"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_45_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_45_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_90 = const()[name = tensor("shape_90"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_44 = const()[name = tensor("reduce_prod_44"), val = tensor(1048576)]; + tensor range_1d_44_start_0 = const()[name = tensor("range_1d_44_start_0"), val = tensor(0)]; + tensor range_1d_44_step_0 = const()[name = tensor("range_1d_44_step_0"), val = tensor(1)]; + tensor range_1d_44 = range_1d(end = reduce_prod_44, start = range_1d_44_start_0, step = range_1d_44_step_0)[name = tensor("range_1d_44")]; + tensor reshape_220 = reshape(shape = shape_90, x = range_1d_44)[name = tensor("reshape_220")]; + tensor slice_by_index_44 = slice_by_index(begin = concat_155, begin_mask = new_cache_45_internal_tensor_assign_1_begin_mask_0, end = concat_156, end_mask = new_cache_45_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_45_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_45_internal_tensor_assign_1_stride_0, x = reshape_220)[name = tensor("slice_by_index_44")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([-1])]; + tensor reshape_221 = reshape(shape = reshape_221_shape_0, x = slice_by_index_44)[name = tensor("reshape_221")]; + tensor reshape_222_shape_0 = const()[name = tensor("reshape_222_shape_0"), val = tensor([-1])]; + tensor reshape_222 = reshape(shape = reshape_222_shape_0, x = var_9681)[name = tensor("reshape_222")]; + tensor reshape_223_shape_0 = const()[name = tensor("reshape_223_shape_0"), val = tensor([-1])]; + tensor reshape_223 = reshape(shape = reshape_223_shape_0, x = cache22)[name = tensor("reshape_223")]; + tensor scatter_44_mode_0 = const()[name = tensor("scatter_44_mode_0"), val = tensor("update")]; + tensor scatter_44_axis_0 = const()[name = tensor("scatter_44_axis_0"), val = tensor(0)]; + tensor scatter_44_validate_indices_0 = const()[name = tensor("scatter_44_validate_indices_0"), val = tensor(false)]; + tensor scatter_44 = scatter(axis = scatter_44_axis_0, data = reshape_223, indices = reshape_221, mode = scatter_44_mode_0, updates = reshape_222, validate_indices = scatter_44_validate_indices_0)[name = tensor("scatter_44")]; + tensor reshape_224 = reshape(shape = shape_90, x = scatter_44)[name = tensor("reshape_224")]; + tensor var_9689_begin_0 = const()[name = tensor("op_9689_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_9689_end_0 = const()[name = tensor("op_9689_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_9689_end_mask_0 = const()[name = tensor("op_9689_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9689_squeeze_mask_0 = const()[name = tensor("op_9689_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9689 = slice_by_index(begin = var_9689_begin_0, end = var_9689_end_0, end_mask = var_9689_end_mask_0, squeeze_mask = var_9689_squeeze_mask_0, x = reshape_224)[name = tensor("op_9689")]; + tensor var_9691_axis_0 = const()[name = tensor("op_9691_axis_0"), val = tensor(1)]; + tensor var_9691_mode_0 = const()[name = tensor("op_9691_mode_0"), val = tensor("update")]; + tensor var_9691_validate_indices_0 = const()[name = tensor("op_9691_validate_indices_0"), val = tensor(false)]; + tensor var_9691 = scatter_along_axis(axis = var_9691_axis_0, data = var_9689, indices = write_indices_45, mode = var_9691_mode_0, updates = v_45, validate_indices = var_9691_validate_indices_0)[name = tensor("op_9691")]; + tensor concat_157 = const()[name = tensor("concat_157"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_158 = const()[name = tensor("concat_158"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_45_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_45_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_91 = const()[name = tensor("shape_91"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_45 = const()[name = tensor("reduce_prod_45"), val = tensor(1048576)]; + tensor range_1d_45_start_0 = const()[name = tensor("range_1d_45_start_0"), val = tensor(0)]; + tensor range_1d_45_step_0 = const()[name = tensor("range_1d_45_step_0"), val = tensor(1)]; + tensor range_1d_45 = range_1d(end = reduce_prod_45, start = range_1d_45_start_0, step = range_1d_45_step_0)[name = tensor("range_1d_45")]; + tensor reshape_225 = reshape(shape = shape_91, x = range_1d_45)[name = tensor("reshape_225")]; + tensor slice_by_index_45 = slice_by_index(begin = concat_157, begin_mask = new_cache_45_internal_tensor_assign_2_begin_mask_0, end = concat_158, end_mask = new_cache_45_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_45_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_45_internal_tensor_assign_2_stride_0, x = reshape_225)[name = tensor("slice_by_index_45")]; + tensor reshape_226_shape_0 = const()[name = tensor("reshape_226_shape_0"), val = tensor([-1])]; + tensor reshape_226 = reshape(shape = reshape_226_shape_0, x = slice_by_index_45)[name = tensor("reshape_226")]; + tensor reshape_227_shape_0 = const()[name = tensor("reshape_227_shape_0"), val = tensor([-1])]; + tensor reshape_227 = reshape(shape = reshape_227_shape_0, x = var_9691)[name = tensor("reshape_227")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([-1])]; + tensor reshape_228 = reshape(shape = reshape_228_shape_0, x = reshape_224)[name = tensor("reshape_228")]; + tensor scatter_45_mode_0 = const()[name = tensor("scatter_45_mode_0"), val = tensor("update")]; + tensor scatter_45_axis_0 = const()[name = tensor("scatter_45_axis_0"), val = tensor(0)]; + tensor scatter_45_validate_indices_0 = const()[name = tensor("scatter_45_validate_indices_0"), val = tensor(false)]; + tensor scatter_45 = scatter(axis = scatter_45_axis_0, data = reshape_228, indices = reshape_226, mode = scatter_45_mode_0, updates = reshape_227, validate_indices = scatter_45_validate_indices_0)[name = tensor("scatter_45")]; + tensor new_cache_45_internal_tensor_assign_2 = reshape(shape = shape_91, x = scatter_45)[name = tensor("reshape_229")]; + tensor keys_133_begin_0 = const()[name = tensor("keys_133_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_133_end_0 = const()[name = tensor("keys_133_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_133_end_mask_0 = const()[name = tensor("keys_133_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_133_squeeze_mask_0 = const()[name = tensor("keys_133_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_133 = slice_by_index(begin = keys_133_begin_0, end = keys_133_end_0, end_mask = keys_133_end_mask_0, squeeze_mask = keys_133_squeeze_mask_0, x = new_cache_45_internal_tensor_assign_2)[name = tensor("keys_133")]; + tensor values_133_begin_0 = const()[name = tensor("values_133_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_133_end_0 = const()[name = tensor("values_133_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_133_end_mask_0 = const()[name = tensor("values_133_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_133_squeeze_mask_0 = const()[name = tensor("values_133_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_133 = slice_by_index(begin = values_133_begin_0, end = values_133_end_0, end_mask = values_133_end_mask_0, squeeze_mask = values_133_squeeze_mask_0, x = new_cache_45_internal_tensor_assign_2)[name = tensor("values_133")]; + tensor var_9703 = not_equal(x = keys_133, y = keys_133)[name = tensor("op_9703")]; + tensor keys_135 = select(a = var_491, b = keys_133, cond = var_9703)[name = tensor("keys_135")]; + tensor var_9711 = not_equal(x = values_133, y = values_133)[name = tensor("op_9711")]; + tensor values_135 = select(a = var_491, b = values_133, cond = var_9711)[name = tensor("values_135")]; + tensor var_9735 = const()[name = tensor("op_9735"), val = tensor([0, 2, 1, 3])]; + tensor var_9748 = const()[name = tensor("op_9748"), val = tensor([1, 1, 1])]; + tensor var_9749 = reshape(shape = var_9748, x = position22)[name = tensor("op_9749")]; + tensor var_9766 = const()[name = tensor("op_9766"), val = tensor(0x1p+0)]; + tensor valid_len_45 = add(x = var_9749, y = var_9766)[name = tensor("valid_len_45")]; + tensor valid_mask_45 = less(x = k_positions_1_promoted, y = valid_len_45)[name = tensor("valid_mask_45")]; + tensor causal_mask_45 = less_equal(x = k_positions_1_promoted, y = var_9749)[name = tensor("causal_mask_45")]; + tensor attn_mask_89 = logical_and(x = valid_mask_45, y = causal_mask_45)[name = tensor("attn_mask_89")]; + tensor attn_mask_91_axes_0 = const()[name = tensor("attn_mask_91_axes_0"), val = tensor([1])]; + tensor attn_mask_91 = expand_dims(axes = attn_mask_91_axes_0, x = attn_mask_89)[name = tensor("attn_mask_91")]; + tensor var_9778 = const()[name = tensor("op_9778"), val = tensor([0x1.fffe5cp-4])]; + tensor var_9784_transpose_x_0 = const()[name = tensor("op_9784_transpose_x_0"), val = tensor(false)]; + tensor var_9784_transpose_y_0 = const()[name = tensor("op_9784_transpose_y_0"), val = tensor(false)]; + tensor transpose_113_perm_0 = const()[name = tensor("transpose_113_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_114_perm_0 = const()[name = tensor("transpose_114_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = keys_135)[name = tensor("transpose_116")]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = q_135)[name = tensor("transpose_117")]; + tensor var_9784 = matmul(transpose_x = var_9784_transpose_x_0, transpose_y = var_9784_transpose_y_0, x = transpose_113, y = transpose_114)[name = tensor("op_9784")]; + tensor attn_weights_133 = mul(x = var_9784, y = var_9778)[name = tensor("attn_weights_133")]; + tensor var_9786 = logical_not(x = attn_mask_91)[name = tensor("op_9786")]; + tensor var_9787 = const()[name = tensor("op_9787"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_135 = select(a = var_9787, b = attn_weights_133, cond = var_9786)[name = tensor("attn_weights_135")]; + tensor var_9789 = const()[name = tensor("op_9789"), val = tensor(-1)]; + tensor attn_weights_137 = softmax(axis = var_9789, x = attn_weights_135)[name = tensor("attn_weights_137")]; + tensor attn_output_45_transpose_x_0 = const()[name = tensor("attn_output_45_transpose_x_0"), val = tensor(false)]; + tensor attn_output_45_transpose_y_0 = const()[name = tensor("attn_output_45_transpose_y_0"), val = tensor(false)]; + tensor values_137 = transpose(perm = var_9735, x = values_135)[name = tensor("transpose_118")]; + tensor attn_output_45 = matmul(transpose_x = attn_output_45_transpose_x_0, transpose_y = attn_output_45_transpose_y_0, x = attn_weights_137, y = values_137)[name = tensor("attn_output_45")]; + tensor var_9797 = const()[name = tensor("op_9797"), val = tensor([0, 2, 1, 3])]; + tensor var_9800 = const()[name = tensor("op_9800"), val = tensor([1, 1, 1024])]; + tensor var_9798 = transpose(perm = var_9797, x = attn_output_45)[name = tensor("transpose_115")]; + tensor input_223 = reshape(shape = var_9800, x = var_9798)[name = tensor("input_223")]; + tensor attn_out_45 = linear(bias = linear_1_bias_0, weight = attn22_out_proj_weight, x = input_223)[name = tensor("linear_89")]; + tensor var_9806 = const()[name = tensor("op_9806"), val = tensor(0x1p+0)]; + tensor var_9807 = add(x = position22, y = var_9806)[name = tensor("op_9807")]; + tensor input_225 = add(x = input_221, y = attn_out_45)[name = tensor("input_225")]; + tensor var_9811 = const()[name = tensor("op_9811"), val = tensor(0x1.4f8b58p-17)]; + tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; + tensor input_227 = layer_norm(axes = input_227_axes_0, beta = norm22_2_bias, epsilon = var_9811, gamma = norm22_2_weight, x = input_225)[name = tensor("input_227")]; + tensor var_9819 = linear(bias = linear_2_bias_0, weight = linear22_1_weight, x = input_227)[name = tensor("linear_90")]; + tensor input_229_mode_0 = const()[name = tensor("input_229_mode_0"), val = tensor("EXACT")]; + tensor input_229 = gelu(mode = input_229_mode_0, x = var_9819)[name = tensor("input_229")]; + tensor ffn_out_45 = linear(bias = linear_1_bias_0, weight = linear22_2_weight, x = input_229)[name = tensor("linear_91")]; + tensor input_231 = add(x = input_225, y = ffn_out_45)[name = tensor("input_231")]; + tensor var_9828 = const()[name = tensor("op_9828"), val = tensor(0x1.4f8b58p-17)]; + tensor x_axes_0 = const()[name = tensor("x_axes_0"), val = tensor([-1])]; + tensor x = layer_norm(axes = x_axes_0, beta = norm23_1_bias, epsilon = var_9828, gamma = norm23_1_weight, x = input_231)[name = tensor("x")]; + tensor var_9849 = linear(bias = linear_0_bias_0, weight = attn23_in_proj_weight, x = x)[name = tensor("linear_92")]; + tensor var_9853 = const()[name = tensor("op_9853"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv = reshape(shape = var_9853, x = var_9849)[name = tensor("qkv")]; + tensor k_93_begin_0 = const()[name = tensor("k_93_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_93_end_0 = const()[name = tensor("k_93_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_93_end_mask_0 = const()[name = tensor("k_93_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_93_squeeze_mask_0 = const()[name = tensor("k_93_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_93 = slice_by_index(begin = k_93_begin_0, end = k_93_end_0, end_mask = k_93_end_mask_0, squeeze_mask = k_93_squeeze_mask_0, x = qkv)[name = tensor("k_93")]; + tensor v_begin_0 = const()[name = tensor("v_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_end_0 = const()[name = tensor("v_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_end_mask_0 = const()[name = tensor("v_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_squeeze_mask_0 = const()[name = tensor("v_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v = slice_by_index(begin = v_begin_0, end = v_end_0, end_mask = v_end_mask_0, squeeze_mask = v_squeeze_mask_0, x = qkv)[name = tensor("v")]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1172744576)))]; + tensor var_9944 = const()[name = tensor("op_9944"), val = tensor([1, 1, 1, 1])]; + tensor ts = reshape(shape = var_9944, x = position23)[name = tensor("ts")]; + tensor var_9948 = const()[name = tensor("op_9948"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex = reshape(shape = var_9948, x = k_93)[name = tensor("k_complex")]; + tensor var_9952_begin_0 = const()[name = tensor("op_9952_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9952_end_0 = const()[name = tensor("op_9952_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9952_end_mask_0 = const()[name = tensor("op_9952_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9952_squeeze_mask_0 = const()[name = tensor("op_9952_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9952 = slice_by_index(begin = var_9952_begin_0, end = var_9952_end_0, end_mask = var_9952_end_mask_0, squeeze_mask = var_9952_squeeze_mask_0, x = k_complex)[name = tensor("op_9952")]; + tensor var_9960_begin_0 = const()[name = tensor("op_9960_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9960_end_0 = const()[name = tensor("op_9960_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9960_end_mask_0 = const()[name = tensor("op_9960_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9960_squeeze_mask_0 = const()[name = tensor("op_9960_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9960 = slice_by_index(begin = var_9960_begin_0, end = var_9960_end_0, end_mask = var_9960_end_mask_0, squeeze_mask = var_9960_squeeze_mask_0, x = k_complex)[name = tensor("op_9960")]; + tensor var_9966 = mul(x = freqs, y = ts)[name = tensor("op_9966")]; + tensor rotr = cos(x = var_9966)[name = tensor("rotr")]; + tensor roti = sin(x = var_9966)[name = tensor("roti")]; + tensor var_9970 = mul(x = var_9952, y = rotr)[name = tensor("op_9970")]; + tensor var_9971 = mul(x = var_9960, y = roti)[name = tensor("op_9971")]; + tensor kor_93 = sub(x = var_9970, y = var_9971)[name = tensor("kor_93")]; + tensor var_9974 = mul(x = var_9952, y = roti)[name = tensor("op_9974")]; + tensor var_9975 = mul(x = var_9960, y = rotr)[name = tensor("op_9975")]; + tensor koi_93 = add(x = var_9974, y = var_9975)[name = tensor("koi_93")]; + tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; + tensor ko = stack(axis = ko_axis_0, values = (kor_93, koi_93))[name = tensor("ko")]; + tensor var_9991 = const()[name = tensor("op_9991"), val = tensor([1, 1, 16, 64])]; + tensor k = reshape(shape = var_9991, x = ko)[name = tensor("k")]; + tensor _inversed_10013_y_0 = const()[name = tensor("_inversed_10013_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_10013 = mul(x = ts, y = _inversed_10013_y_0)[name = tensor("_inversed_10013")]; + tensor var_10014 = floor(x = _inversed_10013)[name = tensor("op_10014")]; + tensor var_10015 = const()[name = tensor("op_10015"), val = tensor(0x1p+9)]; + tensor var_10016 = mul(x = var_10014, y = var_10015)[name = tensor("op_10016")]; + tensor write_indices_float = sub(x = ts, y = var_10016)[name = tensor("write_indices_float")]; + tensor var_10023_dtype_0 = const()[name = tensor("op_10023_dtype_0"), val = tensor("int32")]; + tensor write_indices_reps_0 = const()[name = tensor("write_indices_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_10023 = cast(dtype = var_10023_dtype_0, x = write_indices_float)[name = tensor("cast_423")]; + tensor write_indices = tile(reps = write_indices_reps_0, x = var_10023)[name = tensor("write_indices")]; + tensor var_10031_begin_0 = const()[name = tensor("op_10031_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_10031_end_0 = const()[name = tensor("op_10031_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_10031_end_mask_0 = const()[name = tensor("op_10031_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_10031_squeeze_mask_0 = const()[name = tensor("op_10031_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_10031 = slice_by_index(begin = var_10031_begin_0, end = var_10031_end_0, end_mask = var_10031_end_mask_0, squeeze_mask = var_10031_squeeze_mask_0, x = cache23)[name = tensor("op_10031")]; + tensor var_10033_axis_0 = const()[name = tensor("op_10033_axis_0"), val = tensor(1)]; + tensor var_10033_mode_0 = const()[name = tensor("op_10033_mode_0"), val = tensor("update")]; + tensor var_10033_validate_indices_0 = const()[name = tensor("op_10033_validate_indices_0"), val = tensor(false)]; + tensor var_10033 = scatter_along_axis(axis = var_10033_axis_0, data = var_10031, indices = write_indices, mode = var_10033_mode_0, updates = k, validate_indices = var_10033_validate_indices_0)[name = tensor("op_10033")]; + tensor concat_162 = const()[name = tensor("concat_162"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_163 = const()[name = tensor("concat_163"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_92 = const()[name = tensor("shape_92"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_46 = const()[name = tensor("reduce_prod_46"), val = tensor(1048576)]; + tensor range_1d_46_start_0 = const()[name = tensor("range_1d_46_start_0"), val = tensor(0)]; + tensor range_1d_46_step_0 = const()[name = tensor("range_1d_46_step_0"), val = tensor(1)]; + tensor range_1d_46 = range_1d(end = reduce_prod_46, start = range_1d_46_start_0, step = range_1d_46_step_0)[name = tensor("range_1d_46")]; + tensor reshape_230 = reshape(shape = shape_92, x = range_1d_46)[name = tensor("reshape_230")]; + tensor slice_by_index_46 = slice_by_index(begin = concat_162, begin_mask = new_cache_internal_tensor_assign_1_begin_mask_0, end = concat_163, end_mask = new_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_internal_tensor_assign_1_stride_0, x = reshape_230)[name = tensor("slice_by_index_46")]; + tensor reshape_231_shape_0 = const()[name = tensor("reshape_231_shape_0"), val = tensor([-1])]; + tensor reshape_231 = reshape(shape = reshape_231_shape_0, x = slice_by_index_46)[name = tensor("reshape_231")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([-1])]; + tensor reshape_232 = reshape(shape = reshape_232_shape_0, x = var_10033)[name = tensor("reshape_232")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([-1])]; + tensor reshape_233 = reshape(shape = reshape_233_shape_0, x = cache23)[name = tensor("reshape_233")]; + tensor scatter_46_mode_0 = const()[name = tensor("scatter_46_mode_0"), val = tensor("update")]; + tensor scatter_46_axis_0 = const()[name = tensor("scatter_46_axis_0"), val = tensor(0)]; + tensor scatter_46_validate_indices_0 = const()[name = tensor("scatter_46_validate_indices_0"), val = tensor(false)]; + tensor scatter_46 = scatter(axis = scatter_46_axis_0, data = reshape_233, indices = reshape_231, mode = scatter_46_mode_0, updates = reshape_232, validate_indices = scatter_46_validate_indices_0)[name = tensor("scatter_46")]; + tensor reshape_234 = reshape(shape = shape_92, x = scatter_46)[name = tensor("reshape_234")]; + tensor var_10041_begin_0 = const()[name = tensor("op_10041_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_10041_end_0 = const()[name = tensor("op_10041_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_10041_end_mask_0 = const()[name = tensor("op_10041_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_10041_squeeze_mask_0 = const()[name = tensor("op_10041_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_10041 = slice_by_index(begin = var_10041_begin_0, end = var_10041_end_0, end_mask = var_10041_end_mask_0, squeeze_mask = var_10041_squeeze_mask_0, x = reshape_234)[name = tensor("op_10041")]; + tensor var_10043_axis_0 = const()[name = tensor("op_10043_axis_0"), val = tensor(1)]; + tensor var_10043_mode_0 = const()[name = tensor("op_10043_mode_0"), val = tensor("update")]; + tensor var_10043_validate_indices_0 = const()[name = tensor("op_10043_validate_indices_0"), val = tensor(false)]; + tensor var_10043 = scatter_along_axis(axis = var_10043_axis_0, data = var_10041, indices = write_indices, mode = var_10043_mode_0, updates = v, validate_indices = var_10043_validate_indices_0)[name = tensor("op_10043")]; + tensor concat_164 = const()[name = tensor("concat_164"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_165 = const()[name = tensor("concat_165"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_93 = const()[name = tensor("shape_93"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_47 = const()[name = tensor("reduce_prod_47"), val = tensor(1048576)]; + tensor range_1d_47_start_0 = const()[name = tensor("range_1d_47_start_0"), val = tensor(0)]; + tensor range_1d_47_step_0 = const()[name = tensor("range_1d_47_step_0"), val = tensor(1)]; + tensor range_1d_47 = range_1d(end = reduce_prod_47, start = range_1d_47_start_0, step = range_1d_47_step_0)[name = tensor("range_1d_47")]; + tensor reshape_235 = reshape(shape = shape_93, x = range_1d_47)[name = tensor("reshape_235")]; + tensor slice_by_index_47 = slice_by_index(begin = concat_164, begin_mask = new_cache_internal_tensor_assign_2_begin_mask_0, end = concat_165, end_mask = new_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_internal_tensor_assign_2_stride_0, x = reshape_235)[name = tensor("slice_by_index_47")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([-1])]; + tensor reshape_236 = reshape(shape = reshape_236_shape_0, x = slice_by_index_47)[name = tensor("reshape_236")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([-1])]; + tensor reshape_237 = reshape(shape = reshape_237_shape_0, x = var_10043)[name = tensor("reshape_237")]; + tensor reshape_238_shape_0 = const()[name = tensor("reshape_238_shape_0"), val = tensor([-1])]; + tensor reshape_238 = reshape(shape = reshape_238_shape_0, x = reshape_234)[name = tensor("reshape_238")]; + tensor scatter_47_mode_0 = const()[name = tensor("scatter_47_mode_0"), val = tensor("update")]; + tensor scatter_47_axis_0 = const()[name = tensor("scatter_47_axis_0"), val = tensor(0)]; + tensor scatter_47_validate_indices_0 = const()[name = tensor("scatter_47_validate_indices_0"), val = tensor(false)]; + tensor scatter_47 = scatter(axis = scatter_47_axis_0, data = reshape_238, indices = reshape_236, mode = scatter_47_mode_0, updates = reshape_237, validate_indices = scatter_47_validate_indices_0)[name = tensor("scatter_47")]; + tensor new_cache_internal_tensor_assign_2 = reshape(shape = shape_93, x = scatter_47)[name = tensor("reshape_239")]; + tensor var_10050 = const()[name = tensor("op_10050"), val = tensor(0x1p+0)]; + tensor var_10051 = add(x = position23, y = var_10050)[name = tensor("op_10051")]; + } -> (new_cache_1_internal_tensor_assign_2, var_589, new_cache_3_internal_tensor_assign_2, var_1008, new_cache_5_internal_tensor_assign_2, var_1427, new_cache_7_internal_tensor_assign_2, var_1846, new_cache_9_internal_tensor_assign_2, var_2265, new_cache_11_internal_tensor_assign_2, var_2684, new_cache_13_internal_tensor_assign_2, var_3103, new_cache_15_internal_tensor_assign_2, var_3522, new_cache_17_internal_tensor_assign_2, var_3941, new_cache_19_internal_tensor_assign_2, var_4360, new_cache_21_internal_tensor_assign_2, var_4779, new_cache_23_internal_tensor_assign_2, var_5198, new_cache_25_internal_tensor_assign_2, var_5617, new_cache_27_internal_tensor_assign_2, var_6036, new_cache_29_internal_tensor_assign_2, var_6455, new_cache_31_internal_tensor_assign_2, var_6874, new_cache_33_internal_tensor_assign_2, var_7293, new_cache_35_internal_tensor_assign_2, var_7712, new_cache_37_internal_tensor_assign_2, var_8131, new_cache_39_internal_tensor_assign_2, var_8550, new_cache_41_internal_tensor_assign_2, var_8969, new_cache_43_internal_tensor_assign_2, var_9388, new_cache_45_internal_tensor_assign_2, var_9807, new_cache_internal_tensor_assign_2, var_10051); +} \ No newline at end of file diff --git a/v2/french_24l/cond_step.mlmodelc/weights/weight.bin b/v2/french_24l/cond_step.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a6ea039a8df7e8ecb17f6dc4a5acb3223233f6a3 --- /dev/null +++ b/v2/french_24l/cond_step.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8997761bd4d9635db9c58a27e3d997bd080de95060a98e4ef2551ec9f1f6a715 +size 1172744768 diff --git a/v2/french_24l/cond_step.mlpackage/Data/com.apple.CoreML/model.mlmodel b/v2/french_24l/cond_step.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..88ff7a2b188a243ce2a056a01eca70548c43944e --- /dev/null +++ b/v2/french_24l/cond_step.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f53dfc1df24b97bfb0d3d1fd7581adee111d99b4f60cbb57ff6001c42477ad09 +size 709928 diff --git a/v2/french_24l/cond_step.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/v2/french_24l/cond_step.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a6ea039a8df7e8ecb17f6dc4a5acb3223233f6a3 --- /dev/null +++ b/v2/french_24l/cond_step.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8997761bd4d9635db9c58a27e3d997bd080de95060a98e4ef2551ec9f1f6a715 +size 1172744768 diff --git a/v2/french_24l/cond_step.mlpackage/Manifest.json b/v2/french_24l/cond_step.mlpackage/Manifest.json new file mode 100644 index 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sha256:a83884c5cd8504340a1c8c2c0cfb528a68557be16e6583f5c1cbb127d7e17ced +size 256 diff --git a/v2/french_24l/constants/emb_mean.npy b/v2/french_24l/constants/emb_mean.npy new file mode 100644 index 0000000000000000000000000000000000000000..96d592c8bb9f0527a9ca83dc3a6e8d0a95e6395d --- /dev/null +++ b/v2/french_24l/constants/emb_mean.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97487bd8c7c9d520407abb440d18dffa8de4f96363113effcd5fd50fcd1e5e50 +size 256 diff --git a/v2/french_24l/constants/emb_std.npy b/v2/french_24l/constants/emb_std.npy new file mode 100644 index 0000000000000000000000000000000000000000..a2ae6cb7331ee625bdf997cdd274291dc962f8d4 --- /dev/null +++ b/v2/french_24l/constants/emb_std.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99d58fe567cb5f409bf17d5adfffbe8bade85ee866ea66d0a3e94a0cd3d59e0d +size 256 diff --git a/v2/french_24l/constants/mimi_init_state.npz b/v2/french_24l/constants/mimi_init_state.npz new file 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"computePrecision" : "Mixed (Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-04-24", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1024)", + "shortDescription" : "", + "shape" : "[1, 1024]", + "name" : "transformer_out", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 32)", + "shortDescription" : "", + "shape" : "[1, 32]", + "name" : "latent", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1]", + "name" : "s", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1]", + "name" : "t", + "type" : "MultiArray" + } + ], + "generatedClassName" : "flow_decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/v2/french_24l/flow_decoder.mlmodelc/model.mil b/v2/french_24l/flow_decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0c9057ef8f7cbd4d40e3e5bd0e8667d07047f491 --- /dev/null +++ b/v2/french_24l/flow_decoder.mlmodelc/model.mil @@ -0,0 +1,312 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor latent, tensor s, tensor t, tensor transformer_out) { + tensor flow_net_input_proj_bias = const()[name = tensor("flow_net_input_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor flow_net_input_proj_weight = const()[name = tensor("flow_net_input_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2176)))]; + tensor flow_net_time_embed_0_mlp_0_bias = const()[name = tensor("flow_net_time_embed_0_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67776)))]; + tensor flow_net_time_embed_0_mlp_0_weight = const()[name = tensor("flow_net_time_embed_0_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69888)))]; + tensor flow_net_time_embed_0_mlp_2_bias = const()[name = tensor("flow_net_time_embed_0_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(594240)))]; + tensor flow_net_time_embed_0_mlp_2_weight = const()[name = tensor("flow_net_time_embed_0_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596352)))]; + tensor flow_net_time_embed_1_mlp_0_bias = const()[name = tensor("flow_net_time_embed_1_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644992)))]; + tensor flow_net_time_embed_1_mlp_0_weight = const()[name = tensor("flow_net_time_embed_1_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647104)))]; + tensor flow_net_time_embed_1_mlp_2_bias = const()[name = tensor("flow_net_time_embed_1_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2171456)))]; + tensor flow_net_time_embed_1_mlp_2_weight = const()[name = tensor("flow_net_time_embed_1_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173568)))]; + tensor flow_net_cond_embed_bias = const()[name = tensor("flow_net_cond_embed_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3222208)))]; + tensor flow_net_cond_embed_weight = const()[name = tensor("flow_net_cond_embed_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3224320)))]; + tensor flow_net_res_blocks_0_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_0_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5321536)))]; + tensor flow_net_res_blocks_0_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_0_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5327744)))]; + tensor flow_net_res_blocks_0_in_ln_bias = const()[name = tensor("flow_net_res_blocks_0_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8473536)))]; + tensor flow_net_res_blocks_0_in_ln_weight = const()[name = tensor("flow_net_res_blocks_0_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8475648)))]; + tensor flow_net_res_blocks_0_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_0_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8477760)))]; + tensor flow_net_res_blocks_0_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_0_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8479872)))]; + tensor flow_net_res_blocks_0_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_0_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9528512)))]; + tensor flow_net_res_blocks_0_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_0_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9530624)))]; + tensor flow_net_res_blocks_1_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_1_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10579264)))]; + tensor flow_net_res_blocks_1_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_1_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10585472)))]; + tensor flow_net_res_blocks_1_in_ln_bias = const()[name = tensor("flow_net_res_blocks_1_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13731264)))]; + tensor flow_net_res_blocks_1_in_ln_weight = const()[name = tensor("flow_net_res_blocks_1_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13733376)))]; + tensor flow_net_res_blocks_1_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_1_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13735488)))]; + tensor flow_net_res_blocks_1_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_1_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13737600)))]; + tensor flow_net_res_blocks_1_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_1_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14786240)))]; + tensor flow_net_res_blocks_1_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_1_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14788352)))]; + tensor flow_net_res_blocks_2_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_2_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15836992)))]; + tensor flow_net_res_blocks_2_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_2_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15843200)))]; + tensor flow_net_res_blocks_2_in_ln_bias = const()[name = tensor("flow_net_res_blocks_2_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18988992)))]; + tensor flow_net_res_blocks_2_in_ln_weight = const()[name = tensor("flow_net_res_blocks_2_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18991104)))]; + tensor flow_net_res_blocks_2_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_2_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18993216)))]; + tensor flow_net_res_blocks_2_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_2_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18995328)))]; + tensor flow_net_res_blocks_2_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_2_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20043968)))]; + tensor flow_net_res_blocks_2_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_2_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20046080)))]; + tensor flow_net_res_blocks_3_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_3_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21094720)))]; + tensor flow_net_res_blocks_3_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_3_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21100928)))]; + tensor flow_net_res_blocks_3_in_ln_bias = const()[name = tensor("flow_net_res_blocks_3_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24246720)))]; + tensor flow_net_res_blocks_3_in_ln_weight = const()[name = tensor("flow_net_res_blocks_3_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24248832)))]; + tensor flow_net_res_blocks_3_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_3_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24250944)))]; + tensor flow_net_res_blocks_3_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_3_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24253056)))]; + tensor flow_net_res_blocks_3_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_3_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25301696)))]; + tensor flow_net_res_blocks_3_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_3_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25303808)))]; + tensor flow_net_res_blocks_4_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_4_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26352448)))]; + tensor flow_net_res_blocks_4_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_4_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26358656)))]; + tensor flow_net_res_blocks_4_in_ln_bias = const()[name = tensor("flow_net_res_blocks_4_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29504448)))]; + tensor flow_net_res_blocks_4_in_ln_weight = const()[name = tensor("flow_net_res_blocks_4_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29506560)))]; + tensor flow_net_res_blocks_4_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_4_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29508672)))]; + tensor flow_net_res_blocks_4_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_4_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29510784)))]; + tensor flow_net_res_blocks_4_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_4_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30559424)))]; + tensor flow_net_res_blocks_4_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_4_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30561536)))]; + tensor flow_net_res_blocks_5_adaLN_modulation_1_bias = const()[name = tensor("flow_net_res_blocks_5_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31610176)))]; + tensor flow_net_res_blocks_5_adaLN_modulation_1_weight = const()[name = tensor("flow_net_res_blocks_5_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31616384)))]; + tensor flow_net_res_blocks_5_in_ln_bias = const()[name = tensor("flow_net_res_blocks_5_in_ln_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34762176)))]; + tensor flow_net_res_blocks_5_in_ln_weight = const()[name = tensor("flow_net_res_blocks_5_in_ln_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34764288)))]; + tensor flow_net_res_blocks_5_mlp_0_bias = const()[name = tensor("flow_net_res_blocks_5_mlp_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34766400)))]; + tensor flow_net_res_blocks_5_mlp_0_weight = const()[name = tensor("flow_net_res_blocks_5_mlp_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34768512)))]; + tensor flow_net_res_blocks_5_mlp_2_bias = const()[name = tensor("flow_net_res_blocks_5_mlp_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35817152)))]; + tensor flow_net_res_blocks_5_mlp_2_weight = const()[name = tensor("flow_net_res_blocks_5_mlp_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35819264)))]; + tensor flow_net_final_layer_adaLN_modulation_1_bias = const()[name = tensor("flow_net_final_layer_adaLN_modulation_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36867904)))]; + tensor flow_net_final_layer_adaLN_modulation_1_weight = const()[name = tensor("flow_net_final_layer_adaLN_modulation_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36872064)))]; + tensor flow_net_final_layer_linear_bias = const()[name = tensor("flow_net_final_layer_linear_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38969280)))]; + tensor flow_net_final_layer_linear_weight = const()[name = tensor("flow_net_final_layer_linear_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38969472)))]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(-1)]; + tensor x_5 = linear(bias = flow_net_input_proj_bias, weight = flow_net_input_proj_weight, x = latent)[name = tensor("linear_0")]; + tensor const_0 = const()[name = tensor("const_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39035072)))]; + tensor args_1 = mul(x = s, y = const_0)[name = tensor("args_1")]; + tensor var_39 = cos(x = args_1)[name = tensor("op_39")]; + tensor var_40 = sin(x = args_1)[name = tensor("op_40")]; + tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; + tensor input_1 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_39, var_40))[name = tensor("input_1")]; + tensor input_3 = linear(bias = flow_net_time_embed_0_mlp_0_bias, weight = flow_net_time_embed_0_mlp_0_weight, x = input_1)[name = tensor("linear_1")]; + tensor input_5 = silu(x = input_3)[name = tensor("input_5")]; + tensor x_1 = linear(bias = flow_net_time_embed_0_mlp_2_bias, weight = flow_net_time_embed_0_mlp_2_weight, x = input_5)[name = tensor("linear_2")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([-1])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = x_1)[name = tensor("reduce_mean_0")]; + tensor sub_0 = sub(x = x_1, y = reduce_mean_0)[name = tensor("sub_0")]; + tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_1_axes_0 = const()[name = tensor("reduce_mean_1_axes_0"), val = tensor([-1])]; + tensor reduce_mean_1_keep_dims_0 = const()[name = tensor("reduce_mean_1_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_1 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0)[name = tensor("reduce_mean_1")]; + tensor real_div_0 = const()[name = tensor("real_div_0"), val = tensor(0x1.00804p+0)]; + tensor mul_0 = mul(x = reduce_mean_1, y = real_div_0)[name = tensor("mul_0")]; + tensor var_56 = const()[name = tensor("op_56"), val = tensor(0x1.4f8b58p-17)]; + tensor var_1 = add(x = mul_0, y = var_56)[name = tensor("var_1")]; + tensor const_1 = const()[name = tensor("const_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39035648)))]; + tensor var_59_epsilon_0 = const()[name = tensor("op_59_epsilon_0"), val = tensor(0x1.197998p-40)]; + tensor var_59 = rsqrt(epsilon = var_59_epsilon_0, x = var_1)[name = tensor("op_59")]; + tensor var_60 = mul(x = const_1, y = var_59)[name = tensor("op_60")]; + tensor var_61 = mul(x = x_1, y = var_60)[name = tensor("op_61")]; + tensor args = mul(x = t, y = const_0)[name = tensor("args")]; + tensor var_69 = cos(x = args)[name = tensor("op_69")]; + tensor var_70 = sin(x = args)[name = tensor("op_70")]; + tensor input_7_interleave_0 = const()[name = tensor("input_7_interleave_0"), val = tensor(false)]; + tensor input_7 = concat(axis = var_9, interleave = input_7_interleave_0, values = (var_69, var_70))[name = tensor("input_7")]; + tensor input_9 = linear(bias = flow_net_time_embed_1_mlp_0_bias, weight = flow_net_time_embed_1_mlp_0_weight, x = input_7)[name = tensor("linear_3")]; + tensor input_11 = silu(x = input_9)[name = tensor("input_11")]; + tensor x_3 = linear(bias = flow_net_time_embed_1_mlp_2_bias, weight = flow_net_time_embed_1_mlp_2_weight, x = input_11)[name = tensor("linear_4")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([-1])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = x_3)[name = tensor("reduce_mean_2")]; + tensor sub_2 = sub(x = x_3, y = reduce_mean_2)[name = tensor("sub_2")]; + tensor square_1 = square(x = sub_2)[name = tensor("square_1")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([-1])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = square_1)[name = tensor("reduce_mean_3")]; + tensor real_div_1 = const()[name = tensor("real_div_1"), val = tensor(0x1.00804p+0)]; + tensor mul_1 = mul(x = reduce_mean_3, y = real_div_1)[name = tensor("mul_1")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(0x1.4f8b58p-17)]; + tensor var_3 = add(x = mul_1, y = var_86)[name = tensor("var_3")]; + tensor const_3 = const()[name = tensor("const_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39037760)))]; + tensor var_89_epsilon_0 = const()[name = tensor("op_89_epsilon_0"), val = tensor(0x1.197998p-40)]; + tensor var_89 = rsqrt(epsilon = var_89_epsilon_0, x = var_3)[name = tensor("op_89")]; + tensor var_90 = mul(x = const_3, y = var_89)[name = tensor("op_90")]; + tensor var_91 = mul(x = x_3, y = var_90)[name = tensor("op_91")]; + tensor var_93 = add(x = var_61, y = var_91)[name = tensor("op_93")]; + tensor _inversed_t_combined_y_0 = const()[name = tensor("_inversed_t_combined_y_0"), val = tensor(0x1p-1)]; + tensor _inversed_t_combined = mul(x = var_93, y = _inversed_t_combined_y_0)[name = tensor("_inversed_t_combined")]; + tensor c = linear(bias = flow_net_cond_embed_bias, weight = flow_net_cond_embed_weight, x = transformer_out)[name = tensor("linear_5")]; + tensor input_13 = add(x = _inversed_t_combined, y = c)[name = tensor("input_13")]; + tensor input_15 = silu(x = input_13)[name = tensor("input_15")]; + tensor var_107 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_6")]; + tensor var_108_split_sizes_0 = const()[name = tensor("op_108_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_108_axis_0 = const()[name = tensor("op_108_axis_0"), val = tensor(-1)]; + tensor var_108_0, tensor var_108_1, tensor var_108_2 = split(axis = var_108_axis_0, split_sizes = var_108_split_sizes_0, x = var_107)[name = tensor("op_108")]; + tensor mean_1_axes_0 = const()[name = tensor("mean_1_axes_0"), val = tensor([-1])]; + tensor mean_1_keep_dims_0 = const()[name = tensor("mean_1_keep_dims_0"), val = tensor(true)]; + tensor mean_1 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = x_5)[name = tensor("mean_1")]; + tensor sub_4 = sub(x = x_5, y = mean_1)[name = tensor("sub_4")]; + tensor square_2 = square(x = sub_4)[name = tensor("square_2")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([-1])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_2)[name = tensor("reduce_mean_5")]; + tensor var_118 = const()[name = tensor("op_118"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_119 = add(x = reduce_mean_5, y = var_118)[name = tensor("op_119")]; + tensor var_120 = sqrt(x = var_119)[name = tensor("op_120")]; + tensor x_7 = real_div(x = sub_4, y = var_120)[name = tensor("x_7")]; + tensor var_122 = mul(x = x_7, y = flow_net_res_blocks_0_in_ln_weight)[name = tensor("op_122")]; + tensor x_9 = add(x = var_122, y = flow_net_res_blocks_0_in_ln_bias)[name = tensor("x_9")]; + tensor var_124_promoted = const()[name = tensor("op_124_promoted"), val = tensor(0x1p+0)]; + tensor var_125 = add(x = var_108_1, y = var_124_promoted)[name = tensor("op_125")]; + tensor var_126 = mul(x = x_9, y = var_125)[name = tensor("op_126")]; + tensor input_17 = add(x = var_126, y = var_108_0)[name = tensor("input_17")]; + tensor input_19 = linear(bias = flow_net_res_blocks_0_mlp_0_bias, weight = flow_net_res_blocks_0_mlp_0_weight, x = input_17)[name = tensor("linear_7")]; + tensor input_21 = silu(x = input_19)[name = tensor("input_21")]; + tensor h_1 = linear(bias = flow_net_res_blocks_0_mlp_2_bias, weight = flow_net_res_blocks_0_mlp_2_weight, x = input_21)[name = tensor("linear_8")]; + tensor var_137 = mul(x = var_108_2, y = h_1)[name = tensor("op_137")]; + tensor x_11 = add(x = x_5, y = var_137)[name = tensor("x_11")]; + tensor var_146 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_9")]; + tensor var_147_split_sizes_0 = const()[name = tensor("op_147_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_147_axis_0 = const()[name = tensor("op_147_axis_0"), val = tensor(-1)]; + tensor var_147_0, tensor var_147_1, tensor var_147_2 = split(axis = var_147_axis_0, split_sizes = var_147_split_sizes_0, x = var_146)[name = tensor("op_147")]; + tensor mean_3_axes_0 = const()[name = tensor("mean_3_axes_0"), val = tensor([-1])]; + tensor mean_3_keep_dims_0 = const()[name = tensor("mean_3_keep_dims_0"), val = tensor(true)]; + tensor mean_3 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = x_11)[name = tensor("mean_3")]; + tensor sub_5 = sub(x = x_11, y = mean_3)[name = tensor("sub_5")]; + tensor square_3 = square(x = sub_5)[name = tensor("square_3")]; + tensor reduce_mean_7_axes_0 = const()[name = tensor("reduce_mean_7_axes_0"), val = tensor([-1])]; + tensor reduce_mean_7_keep_dims_0 = const()[name = tensor("reduce_mean_7_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_7 = reduce_mean(axes = reduce_mean_7_axes_0, keep_dims = reduce_mean_7_keep_dims_0, x = square_3)[name = tensor("reduce_mean_7")]; + tensor var_157 = const()[name = tensor("op_157"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_158 = add(x = reduce_mean_7, y = var_157)[name = tensor("op_158")]; + tensor var_159 = sqrt(x = var_158)[name = tensor("op_159")]; + tensor x_13 = real_div(x = sub_5, y = var_159)[name = tensor("x_13")]; + tensor var_161 = mul(x = x_13, y = flow_net_res_blocks_1_in_ln_weight)[name = tensor("op_161")]; + tensor x_15 = add(x = var_161, y = flow_net_res_blocks_1_in_ln_bias)[name = tensor("x_15")]; + tensor var_163_promoted = const()[name = tensor("op_163_promoted"), val = tensor(0x1p+0)]; + tensor var_164 = add(x = var_147_1, y = var_163_promoted)[name = tensor("op_164")]; + tensor var_165 = mul(x = x_15, y = var_164)[name = tensor("op_165")]; + tensor input_25 = add(x = var_165, y = var_147_0)[name = tensor("input_25")]; + tensor input_27 = linear(bias = flow_net_res_blocks_1_mlp_0_bias, weight = flow_net_res_blocks_1_mlp_0_weight, x = input_25)[name = tensor("linear_10")]; + tensor input_29 = silu(x = input_27)[name = tensor("input_29")]; + tensor h_3 = linear(bias = flow_net_res_blocks_1_mlp_2_bias, weight = flow_net_res_blocks_1_mlp_2_weight, x = input_29)[name = tensor("linear_11")]; + tensor var_176 = mul(x = var_147_2, y = h_3)[name = tensor("op_176")]; + tensor x_17 = add(x = x_11, y = var_176)[name = tensor("x_17")]; + tensor var_185 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_12")]; + tensor var_186_split_sizes_0 = const()[name = tensor("op_186_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_186_axis_0 = const()[name = tensor("op_186_axis_0"), val = tensor(-1)]; + tensor var_186_0, tensor var_186_1, tensor var_186_2 = split(axis = var_186_axis_0, split_sizes = var_186_split_sizes_0, x = var_185)[name = tensor("op_186")]; + tensor mean_5_axes_0 = const()[name = tensor("mean_5_axes_0"), val = tensor([-1])]; + tensor mean_5_keep_dims_0 = const()[name = tensor("mean_5_keep_dims_0"), val = tensor(true)]; + tensor mean_5 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = x_17)[name = tensor("mean_5")]; + tensor sub_6 = sub(x = x_17, y = mean_5)[name = tensor("sub_6")]; + tensor square_4 = square(x = sub_6)[name = tensor("square_4")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([-1])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = square_4)[name = tensor("reduce_mean_9")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_197 = add(x = reduce_mean_9, y = var_196)[name = tensor("op_197")]; + tensor var_198 = sqrt(x = var_197)[name = tensor("op_198")]; + tensor x_19 = real_div(x = sub_6, y = var_198)[name = tensor("x_19")]; + tensor var_200 = mul(x = x_19, y = flow_net_res_blocks_2_in_ln_weight)[name = tensor("op_200")]; + tensor x_21 = add(x = var_200, y = flow_net_res_blocks_2_in_ln_bias)[name = tensor("x_21")]; + tensor var_202_promoted = const()[name = tensor("op_202_promoted"), val = tensor(0x1p+0)]; + tensor var_203 = add(x = var_186_1, y = var_202_promoted)[name = tensor("op_203")]; + tensor var_204 = mul(x = x_21, y = var_203)[name = tensor("op_204")]; + tensor input_33 = add(x = var_204, y = var_186_0)[name = tensor("input_33")]; + tensor input_35 = linear(bias = flow_net_res_blocks_2_mlp_0_bias, weight = flow_net_res_blocks_2_mlp_0_weight, x = input_33)[name = tensor("linear_13")]; + tensor input_37 = silu(x = input_35)[name = tensor("input_37")]; + tensor h_5 = linear(bias = flow_net_res_blocks_2_mlp_2_bias, weight = flow_net_res_blocks_2_mlp_2_weight, x = input_37)[name = tensor("linear_14")]; + tensor var_215 = mul(x = var_186_2, y = h_5)[name = tensor("op_215")]; + tensor x_23 = add(x = x_17, y = var_215)[name = tensor("x_23")]; + tensor var_224 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_15")]; + tensor var_225_split_sizes_0 = const()[name = tensor("op_225_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_225_axis_0 = const()[name = tensor("op_225_axis_0"), val = tensor(-1)]; + tensor var_225_0, tensor var_225_1, tensor var_225_2 = split(axis = var_225_axis_0, split_sizes = var_225_split_sizes_0, x = var_224)[name = tensor("op_225")]; + tensor mean_7_axes_0 = const()[name = tensor("mean_7_axes_0"), val = tensor([-1])]; + tensor mean_7_keep_dims_0 = const()[name = tensor("mean_7_keep_dims_0"), val = tensor(true)]; + tensor mean_7 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = x_23)[name = tensor("mean_7")]; + tensor sub_7 = sub(x = x_23, y = mean_7)[name = tensor("sub_7")]; + tensor square_5 = square(x = sub_7)[name = tensor("square_5")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([-1])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_5)[name = tensor("reduce_mean_11")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_236 = add(x = reduce_mean_11, y = var_235)[name = tensor("op_236")]; + tensor var_237 = sqrt(x = var_236)[name = tensor("op_237")]; + tensor x_25 = real_div(x = sub_7, y = var_237)[name = tensor("x_25")]; + tensor var_239 = mul(x = x_25, y = flow_net_res_blocks_3_in_ln_weight)[name = tensor("op_239")]; + tensor x_27 = add(x = var_239, y = flow_net_res_blocks_3_in_ln_bias)[name = tensor("x_27")]; + tensor var_241_promoted = const()[name = tensor("op_241_promoted"), val = tensor(0x1p+0)]; + tensor var_242 = add(x = var_225_1, y = var_241_promoted)[name = tensor("op_242")]; + tensor var_243 = mul(x = x_27, y = var_242)[name = tensor("op_243")]; + tensor input_41 = add(x = var_243, y = var_225_0)[name = tensor("input_41")]; + tensor input_43 = linear(bias = flow_net_res_blocks_3_mlp_0_bias, weight = flow_net_res_blocks_3_mlp_0_weight, x = input_41)[name = tensor("linear_16")]; + tensor input_45 = silu(x = input_43)[name = tensor("input_45")]; + tensor h_7 = linear(bias = flow_net_res_blocks_3_mlp_2_bias, weight = flow_net_res_blocks_3_mlp_2_weight, x = input_45)[name = tensor("linear_17")]; + tensor var_254 = mul(x = var_225_2, y = h_7)[name = tensor("op_254")]; + tensor x_29 = add(x = x_23, y = var_254)[name = tensor("x_29")]; + tensor var_263 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_18")]; + tensor var_264_split_sizes_0 = const()[name = tensor("op_264_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_264_axis_0 = const()[name = tensor("op_264_axis_0"), val = tensor(-1)]; + tensor var_264_0, tensor var_264_1, tensor var_264_2 = split(axis = var_264_axis_0, split_sizes = var_264_split_sizes_0, x = var_263)[name = tensor("op_264")]; + tensor mean_9_axes_0 = const()[name = tensor("mean_9_axes_0"), val = tensor([-1])]; + tensor mean_9_keep_dims_0 = const()[name = tensor("mean_9_keep_dims_0"), val = tensor(true)]; + tensor mean_9 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = x_29)[name = tensor("mean_9")]; + tensor sub_8 = sub(x = x_29, y = mean_9)[name = tensor("sub_8")]; + tensor square_6 = square(x = sub_8)[name = tensor("square_6")]; + tensor reduce_mean_13_axes_0 = const()[name = tensor("reduce_mean_13_axes_0"), val = tensor([-1])]; + tensor reduce_mean_13_keep_dims_0 = const()[name = tensor("reduce_mean_13_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_13 = reduce_mean(axes = reduce_mean_13_axes_0, keep_dims = reduce_mean_13_keep_dims_0, x = square_6)[name = tensor("reduce_mean_13")]; + tensor var_274 = const()[name = tensor("op_274"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_275 = add(x = reduce_mean_13, y = var_274)[name = tensor("op_275")]; + tensor var_276 = sqrt(x = var_275)[name = tensor("op_276")]; + tensor x_31 = real_div(x = sub_8, y = var_276)[name = tensor("x_31")]; + tensor var_278 = mul(x = x_31, y = flow_net_res_blocks_4_in_ln_weight)[name = tensor("op_278")]; + tensor x_33 = add(x = var_278, y = flow_net_res_blocks_4_in_ln_bias)[name = tensor("x_33")]; + tensor var_280_promoted = const()[name = tensor("op_280_promoted"), val = tensor(0x1p+0)]; + tensor var_281 = add(x = var_264_1, y = var_280_promoted)[name = tensor("op_281")]; + tensor var_282 = mul(x = x_33, y = var_281)[name = tensor("op_282")]; + tensor input_49 = add(x = var_282, y = var_264_0)[name = tensor("input_49")]; + tensor input_51 = linear(bias = flow_net_res_blocks_4_mlp_0_bias, weight = flow_net_res_blocks_4_mlp_0_weight, x = input_49)[name = tensor("linear_19")]; + tensor input_53 = silu(x = input_51)[name = tensor("input_53")]; + tensor h_9 = linear(bias = flow_net_res_blocks_4_mlp_2_bias, weight = flow_net_res_blocks_4_mlp_2_weight, x = input_53)[name = tensor("linear_20")]; + tensor var_293 = mul(x = var_264_2, y = h_9)[name = tensor("op_293")]; + tensor x_35 = add(x = x_29, y = var_293)[name = tensor("x_35")]; + tensor var_302 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_21")]; + tensor var_303_split_sizes_0 = const()[name = tensor("op_303_split_sizes_0"), val = tensor([512, 512, 512])]; + tensor var_303_axis_0 = const()[name = tensor("op_303_axis_0"), val = tensor(-1)]; + tensor var_303_0, tensor var_303_1, tensor var_303_2 = split(axis = var_303_axis_0, split_sizes = var_303_split_sizes_0, x = var_302)[name = tensor("op_303")]; + tensor mean_11_axes_0 = const()[name = tensor("mean_11_axes_0"), val = tensor([-1])]; + tensor mean_11_keep_dims_0 = const()[name = tensor("mean_11_keep_dims_0"), val = tensor(true)]; + tensor mean_11 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = x_35)[name = tensor("mean_11")]; + tensor sub_9 = sub(x = x_35, y = mean_11)[name = tensor("sub_9")]; + tensor square_7 = square(x = sub_9)[name = tensor("square_7")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([-1])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = square_7)[name = tensor("reduce_mean_15")]; + tensor var_313 = const()[name = tensor("op_313"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_314 = add(x = reduce_mean_15, y = var_313)[name = tensor("op_314")]; + tensor var_315 = sqrt(x = var_314)[name = tensor("op_315")]; + tensor x_37 = real_div(x = sub_9, y = var_315)[name = tensor("x_37")]; + tensor var_317 = mul(x = x_37, y = flow_net_res_blocks_5_in_ln_weight)[name = tensor("op_317")]; + tensor x_39 = add(x = var_317, y = flow_net_res_blocks_5_in_ln_bias)[name = tensor("x_39")]; + tensor var_319_promoted = const()[name = tensor("op_319_promoted"), val = tensor(0x1p+0)]; + tensor var_320 = add(x = var_303_1, y = var_319_promoted)[name = tensor("op_320")]; + tensor var_321 = mul(x = x_39, y = var_320)[name = tensor("op_321")]; + tensor input_57 = add(x = var_321, y = var_303_0)[name = tensor("input_57")]; + tensor input_59 = linear(bias = flow_net_res_blocks_5_mlp_0_bias, weight = flow_net_res_blocks_5_mlp_0_weight, x = input_57)[name = tensor("linear_22")]; + tensor input_61 = silu(x = input_59)[name = tensor("input_61")]; + tensor h = linear(bias = flow_net_res_blocks_5_mlp_2_bias, weight = flow_net_res_blocks_5_mlp_2_weight, x = input_61)[name = tensor("linear_23")]; + tensor var_332 = mul(x = var_303_2, y = h)[name = tensor("op_332")]; + tensor x_41 = add(x = x_35, y = var_332)[name = tensor("x_41")]; + tensor var_340 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias, weight = flow_net_final_layer_adaLN_modulation_1_weight, x = input_15)[name = tensor("linear_24")]; + tensor var_341_split_sizes_0 = const()[name = tensor("op_341_split_sizes_0"), val = tensor([512, 512])]; + tensor var_341_axis_0 = const()[name = tensor("op_341_axis_0"), val = tensor(-1)]; + tensor var_341_0, tensor var_341_1 = split(axis = var_341_axis_0, split_sizes = var_341_split_sizes_0, x = var_340)[name = tensor("op_341")]; + tensor mean_axes_0 = const()[name = tensor("mean_axes_0"), val = tensor([-1])]; + tensor mean_keep_dims_0 = const()[name = tensor("mean_keep_dims_0"), val = tensor(true)]; + tensor mean = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = x_41)[name = tensor("mean")]; + tensor sub_10 = sub(x = x_41, y = mean)[name = tensor("sub_10")]; + tensor square_8 = square(x = sub_10)[name = tensor("square_8")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([-1])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_8)[name = tensor("reduce_mean_17")]; + tensor var_348 = const()[name = tensor("op_348"), val = tensor(0x1.0c6f7ap-20)]; + tensor var_349 = add(x = reduce_mean_17, y = var_348)[name = tensor("op_349")]; + tensor var_350 = sqrt(x = var_349)[name = tensor("op_350")]; + tensor x = real_div(x = sub_10, y = var_350)[name = tensor("x")]; + tensor var_352_promoted = const()[name = tensor("op_352_promoted"), val = tensor(0x1p+0)]; + tensor var_353 = add(x = var_341_1, y = var_352_promoted)[name = tensor("op_353")]; + tensor var_354 = mul(x = x, y = var_353)[name = tensor("op_354")]; + tensor input = add(x = var_354, y = var_341_0)[name = tensor("input")]; + tensor var_358 = linear(bias = flow_net_final_layer_linear_bias, weight = flow_net_final_layer_linear_weight, x = input)[name = tensor("linear_25")]; + } -> 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{"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor bos_emb, tensor cache0, tensor cache1, tensor cache10, tensor cache11, tensor cache12, tensor cache13, tensor cache14, tensor cache15, tensor cache16, tensor cache17, tensor cache18, tensor cache19, tensor cache2, tensor cache20, tensor cache21, tensor cache22, tensor cache23, tensor cache3, tensor cache4, tensor cache5, tensor cache6, tensor cache7, tensor cache8, tensor cache9, tensor position0, tensor position1, tensor position10, tensor position11, tensor position12, tensor position13, tensor position14, tensor position15, tensor position16, tensor position17, tensor position18, tensor position19, tensor position2, tensor position20, tensor position21, tensor position22, tensor position23, tensor position3, tensor position4, tensor position5, tensor position6, tensor position7, tensor position8, tensor position9, tensor sequence) { + tensor input_linear_weight = const()[name = tensor("input_linear_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor norm0_1_bias = const()[name = tensor("norm0_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131200)))]; + tensor norm0_1_weight = const()[name = tensor("norm0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135360)))]; + tensor attn0_in_proj_weight = const()[name = tensor("attn0_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139520)))]; + tensor attn0_out_proj_weight = const()[name = tensor("attn0_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12722496)))]; + tensor norm0_2_bias = const()[name = tensor("norm0_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16916864)))]; + tensor norm0_2_weight = const()[name = tensor("norm0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16921024)))]; + tensor linear0_1_weight = const()[name = tensor("linear0_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16925184)))]; + tensor linear0_2_weight = const()[name = tensor("linear0_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33702464)))]; + tensor norm1_1_bias = const()[name = tensor("norm1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50479744)))]; + tensor norm1_1_weight = const()[name = tensor("norm1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50483904)))]; + tensor attn1_in_proj_weight = const()[name = tensor("attn1_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50488064)))]; + tensor attn1_out_proj_weight = const()[name = tensor("attn1_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63071040)))]; + tensor norm1_2_bias = const()[name = tensor("norm1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67265408)))]; + tensor norm1_2_weight = const()[name = tensor("norm1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67269568)))]; + tensor linear1_1_weight = const()[name = tensor("linear1_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67273728)))]; + tensor linear1_2_weight = const()[name = tensor("linear1_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84051008)))]; + tensor norm2_1_bias = const()[name = tensor("norm2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100828288)))]; + tensor norm2_1_weight = const()[name = tensor("norm2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100832448)))]; + tensor attn2_in_proj_weight = const()[name = tensor("attn2_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100836608)))]; + tensor attn2_out_proj_weight = const()[name = tensor("attn2_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113419584)))]; + tensor norm2_2_bias = const()[name = tensor("norm2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117613952)))]; + tensor norm2_2_weight = const()[name = tensor("norm2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117618112)))]; + tensor linear2_1_weight = const()[name = tensor("linear2_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117622272)))]; + tensor linear2_2_weight = const()[name = tensor("linear2_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134399552)))]; + tensor norm3_1_bias = const()[name = tensor("norm3_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151176832)))]; + tensor norm3_1_weight = const()[name = tensor("norm3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151180992)))]; + tensor attn3_in_proj_weight = const()[name = tensor("attn3_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151185152)))]; + tensor attn3_out_proj_weight = const()[name = tensor("attn3_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163768128)))]; + tensor norm3_2_bias = const()[name = tensor("norm3_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167962496)))]; + tensor norm3_2_weight = const()[name = tensor("norm3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167966656)))]; + tensor linear3_1_weight = const()[name = tensor("linear3_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167970816)))]; + tensor linear3_2_weight = const()[name = tensor("linear3_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184748096)))]; + tensor norm4_1_bias = const()[name = tensor("norm4_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201525376)))]; + tensor norm4_1_weight = const()[name = tensor("norm4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201529536)))]; + tensor attn4_in_proj_weight = const()[name = tensor("attn4_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201533696)))]; + tensor attn4_out_proj_weight = const()[name = tensor("attn4_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214116672)))]; + tensor norm4_2_bias = const()[name = tensor("norm4_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218311040)))]; + tensor norm4_2_weight = const()[name = tensor("norm4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218315200)))]; + tensor linear4_1_weight = const()[name = tensor("linear4_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218319360)))]; + tensor linear4_2_weight = const()[name = tensor("linear4_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235096640)))]; + tensor norm5_1_bias = const()[name = tensor("norm5_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251873920)))]; + tensor norm5_1_weight = const()[name = tensor("norm5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251878080)))]; + tensor attn5_in_proj_weight = const()[name = tensor("attn5_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251882240)))]; + tensor attn5_out_proj_weight = const()[name = tensor("attn5_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264465216)))]; + tensor norm5_2_bias = const()[name = tensor("norm5_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268659584)))]; + tensor norm5_2_weight = const()[name = tensor("norm5_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268663744)))]; + tensor linear5_1_weight = const()[name = tensor("linear5_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268667904)))]; + tensor linear5_2_weight = const()[name = tensor("linear5_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285445184)))]; + tensor norm6_1_bias = const()[name = tensor("norm6_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302222464)))]; + tensor norm6_1_weight = const()[name = tensor("norm6_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302226624)))]; + tensor attn6_in_proj_weight = const()[name = tensor("attn6_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302230784)))]; + tensor attn6_out_proj_weight = const()[name = tensor("attn6_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314813760)))]; + tensor norm6_2_bias = const()[name = tensor("norm6_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319008128)))]; + tensor norm6_2_weight = const()[name = tensor("norm6_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319012288)))]; + tensor linear6_1_weight = const()[name = tensor("linear6_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319016448)))]; + tensor linear6_2_weight = const()[name = tensor("linear6_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335793728)))]; + tensor norm7_1_bias = const()[name = tensor("norm7_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(352571008)))]; + tensor norm7_1_weight = const()[name = tensor("norm7_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(352575168)))]; + tensor attn7_in_proj_weight = const()[name = tensor("attn7_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(352579328)))]; + tensor attn7_out_proj_weight = const()[name = tensor("attn7_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365162304)))]; + tensor norm7_2_bias = const()[name = tensor("norm7_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369356672)))]; + tensor norm7_2_weight = const()[name = tensor("norm7_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369360832)))]; + tensor linear7_1_weight = const()[name = tensor("linear7_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369364992)))]; + tensor linear7_2_weight = const()[name = tensor("linear7_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386142272)))]; + tensor norm8_1_bias = const()[name = tensor("norm8_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402919552)))]; + tensor norm8_1_weight = const()[name = tensor("norm8_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402923712)))]; + tensor attn8_in_proj_weight = const()[name = tensor("attn8_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402927872)))]; + tensor attn8_out_proj_weight = const()[name = tensor("attn8_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415510848)))]; + tensor norm8_2_bias = const()[name = tensor("norm8_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419705216)))]; + tensor norm8_2_weight = const()[name = tensor("norm8_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419709376)))]; + tensor linear8_1_weight = const()[name = tensor("linear8_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419713536)))]; + tensor linear8_2_weight = const()[name = tensor("linear8_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436490816)))]; + tensor norm9_1_bias = const()[name = tensor("norm9_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(453268096)))]; + tensor norm9_1_weight = const()[name = tensor("norm9_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(453272256)))]; + tensor attn9_in_proj_weight = const()[name = tensor("attn9_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(453276416)))]; + tensor attn9_out_proj_weight = const()[name = tensor("attn9_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465859392)))]; + tensor norm9_2_bias = const()[name = tensor("norm9_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470053760)))]; + tensor norm9_2_weight = const()[name = tensor("norm9_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470057920)))]; + tensor linear9_1_weight = const()[name = tensor("linear9_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470062080)))]; + tensor linear9_2_weight = const()[name = tensor("linear9_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486839360)))]; + tensor norm10_1_bias = const()[name = tensor("norm10_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503616640)))]; + tensor norm10_1_weight = const()[name = tensor("norm10_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503620800)))]; + tensor attn10_in_proj_weight = const()[name = tensor("attn10_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503624960)))]; + tensor attn10_out_proj_weight = const()[name = tensor("attn10_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(516207936)))]; + tensor norm10_2_bias = const()[name = tensor("norm10_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520402304)))]; + tensor norm10_2_weight = const()[name = tensor("norm10_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520406464)))]; + tensor linear10_1_weight = const()[name = tensor("linear10_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520410624)))]; + tensor linear10_2_weight = const()[name = tensor("linear10_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537187904)))]; + tensor norm11_1_bias = const()[name = tensor("norm11_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553965184)))]; + tensor norm11_1_weight = const()[name = tensor("norm11_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553969344)))]; + tensor attn11_in_proj_weight = const()[name = tensor("attn11_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553973504)))]; + tensor attn11_out_proj_weight = const()[name = tensor("attn11_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566556480)))]; + tensor norm11_2_bias = const()[name = tensor("norm11_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570750848)))]; + tensor norm11_2_weight = const()[name = tensor("norm11_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570755008)))]; + tensor linear11_1_weight = const()[name = tensor("linear11_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570759168)))]; + tensor linear11_2_weight = const()[name = tensor("linear11_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(587536448)))]; + tensor norm12_1_bias = const()[name = tensor("norm12_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(604313728)))]; + tensor norm12_1_weight = const()[name = tensor("norm12_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(604317888)))]; + tensor attn12_in_proj_weight = const()[name = tensor("attn12_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(604322048)))]; + tensor attn12_out_proj_weight = const()[name = tensor("attn12_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(616905024)))]; + tensor norm12_2_bias = const()[name = tensor("norm12_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621099392)))]; + tensor norm12_2_weight = const()[name = tensor("norm12_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621103552)))]; + tensor linear12_1_weight = const()[name = tensor("linear12_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621107712)))]; + tensor linear12_2_weight = const()[name = tensor("linear12_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637884992)))]; + tensor norm13_1_bias = const()[name = tensor("norm13_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654662272)))]; + tensor norm13_1_weight = const()[name = tensor("norm13_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654666432)))]; + tensor attn13_in_proj_weight = const()[name = tensor("attn13_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654670592)))]; + tensor attn13_out_proj_weight = const()[name = tensor("attn13_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(667253568)))]; + tensor norm13_2_bias = const()[name = tensor("norm13_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671447936)))]; + tensor norm13_2_weight = const()[name = tensor("norm13_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671452096)))]; + tensor linear13_1_weight = const()[name = tensor("linear13_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671456256)))]; + tensor linear13_2_weight = const()[name = tensor("linear13_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688233536)))]; + tensor norm14_1_bias = const()[name = tensor("norm14_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(705010816)))]; + tensor norm14_1_weight = const()[name = tensor("norm14_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(705014976)))]; + tensor attn14_in_proj_weight = const()[name = tensor("attn14_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(705019136)))]; + tensor attn14_out_proj_weight = const()[name = tensor("attn14_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717602112)))]; + tensor norm14_2_bias = const()[name = tensor("norm14_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721796480)))]; + tensor norm14_2_weight = const()[name = tensor("norm14_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721800640)))]; + tensor linear14_1_weight = const()[name = tensor("linear14_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721804800)))]; + tensor linear14_2_weight = const()[name = tensor("linear14_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(738582080)))]; + tensor norm15_1_bias = const()[name = tensor("norm15_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755359360)))]; + tensor norm15_1_weight = const()[name = tensor("norm15_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755363520)))]; + tensor attn15_in_proj_weight = const()[name = tensor("attn15_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755367680)))]; + tensor attn15_out_proj_weight = const()[name = tensor("attn15_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767950656)))]; + tensor norm15_2_bias = const()[name = tensor("norm15_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772145024)))]; + tensor norm15_2_weight = const()[name = tensor("norm15_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772149184)))]; + tensor linear15_1_weight = const()[name = tensor("linear15_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772153344)))]; + tensor linear15_2_weight = const()[name = tensor("linear15_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788930624)))]; + tensor norm16_1_bias = const()[name = tensor("norm16_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805707904)))]; + tensor norm16_1_weight = const()[name = tensor("norm16_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805712064)))]; + tensor attn16_in_proj_weight = const()[name = tensor("attn16_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805716224)))]; + tensor attn16_out_proj_weight = const()[name = tensor("attn16_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818299200)))]; + tensor norm16_2_bias = const()[name = tensor("norm16_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822493568)))]; + tensor norm16_2_weight = const()[name = tensor("norm16_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822497728)))]; + tensor linear16_1_weight = const()[name = tensor("linear16_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822501888)))]; + tensor linear16_2_weight = const()[name = tensor("linear16_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(839279168)))]; + tensor norm17_1_bias = const()[name = tensor("norm17_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856056448)))]; + tensor norm17_1_weight = const()[name = tensor("norm17_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856060608)))]; + tensor attn17_in_proj_weight = const()[name = tensor("attn17_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856064768)))]; + tensor attn17_out_proj_weight = const()[name = tensor("attn17_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(868647744)))]; + tensor norm17_2_bias = const()[name = tensor("norm17_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(872842112)))]; + tensor norm17_2_weight = const()[name = tensor("norm17_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(872846272)))]; + tensor linear17_1_weight = const()[name = tensor("linear17_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(872850432)))]; + tensor linear17_2_weight = const()[name = tensor("linear17_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(889627712)))]; + tensor norm18_1_bias = const()[name = tensor("norm18_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(906404992)))]; + tensor norm18_1_weight = const()[name = tensor("norm18_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(906409152)))]; + tensor attn18_in_proj_weight = const()[name = tensor("attn18_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(906413312)))]; + tensor attn18_out_proj_weight = const()[name = tensor("attn18_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(918996288)))]; + tensor norm18_2_bias = const()[name = tensor("norm18_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923190656)))]; + tensor norm18_2_weight = const()[name = tensor("norm18_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923194816)))]; + tensor linear18_1_weight = const()[name = tensor("linear18_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923198976)))]; + tensor linear18_2_weight = const()[name = tensor("linear18_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939976256)))]; + tensor norm19_1_bias = const()[name = tensor("norm19_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956753536)))]; + tensor norm19_1_weight = const()[name = tensor("norm19_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956757696)))]; + tensor attn19_in_proj_weight = const()[name = tensor("attn19_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956761856)))]; + tensor attn19_out_proj_weight = const()[name = tensor("attn19_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(969344832)))]; + tensor norm19_2_bias = const()[name = tensor("norm19_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973539200)))]; + tensor norm19_2_weight = const()[name = tensor("norm19_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973543360)))]; + tensor linear19_1_weight = const()[name = tensor("linear19_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973547520)))]; + tensor linear19_2_weight = const()[name = tensor("linear19_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(990324800)))]; + tensor norm20_1_bias = const()[name = tensor("norm20_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007102080)))]; + tensor norm20_1_weight = const()[name = tensor("norm20_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007106240)))]; + tensor attn20_in_proj_weight = const()[name = tensor("attn20_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007110400)))]; + tensor attn20_out_proj_weight = const()[name = tensor("attn20_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1019693376)))]; + tensor norm20_2_bias = const()[name = tensor("norm20_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023887744)))]; + tensor norm20_2_weight = const()[name = tensor("norm20_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023891904)))]; + tensor linear20_1_weight = const()[name = tensor("linear20_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023896064)))]; + tensor linear20_2_weight = const()[name = tensor("linear20_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1040673344)))]; + tensor norm21_1_bias = const()[name = tensor("norm21_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057450624)))]; + tensor norm21_1_weight = const()[name = tensor("norm21_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057454784)))]; + tensor attn21_in_proj_weight = const()[name = tensor("attn21_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057458944)))]; + tensor attn21_out_proj_weight = const()[name = tensor("attn21_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070041920)))]; + tensor norm21_2_bias = const()[name = tensor("norm21_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074236288)))]; + tensor norm21_2_weight = const()[name = tensor("norm21_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074240448)))]; + tensor linear21_1_weight = const()[name = tensor("linear21_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074244608)))]; + tensor linear21_2_weight = const()[name = tensor("linear21_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1091021888)))]; + tensor norm22_1_bias = const()[name = tensor("norm22_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107799168)))]; + tensor norm22_1_weight = const()[name = tensor("norm22_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107803328)))]; + tensor attn22_in_proj_weight = const()[name = tensor("attn22_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107807488)))]; + tensor attn22_out_proj_weight = const()[name = tensor("attn22_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1120390464)))]; + tensor norm22_2_bias = const()[name = tensor("norm22_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1124584832)))]; + tensor norm22_2_weight = const()[name = tensor("norm22_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1124588992)))]; + tensor linear22_1_weight = const()[name = tensor("linear22_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1124593152)))]; + tensor linear22_2_weight = const()[name = tensor("linear22_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1141370432)))]; + tensor norm23_1_bias = const()[name = tensor("norm23_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158147712)))]; + tensor norm23_1_weight = const()[name = tensor("norm23_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158151872)))]; + tensor attn23_in_proj_weight = const()[name = tensor("attn23_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158156032)))]; + tensor attn23_out_proj_weight = const()[name = tensor("attn23_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170739008)))]; + tensor norm23_2_bias = const()[name = tensor("norm23_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1174933376)))]; + tensor norm23_2_weight = const()[name = tensor("norm23_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1174937536)))]; + tensor linear23_1_weight = const()[name = tensor("linear23_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1174941696)))]; + tensor linear23_2_weight = const()[name = tensor("linear23_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191718976)))]; + tensor out_norm_bias = const()[name = tensor("out_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208496256)))]; + tensor out_norm_weight = const()[name = tensor("out_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208500416)))]; + tensor out_eos_bias = const()[name = tensor("out_eos_bias"), val = tensor([-0x1.8ap-3])]; + tensor out_eos_weight = const()[name = tensor("out_eos_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208504576)))]; + tensor var_198 = not_equal(x = sequence, y = sequence)[name = tensor("op_198")]; + tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0, 1])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = bos_emb)[name = tensor("expand_dims_0")]; + tensor input_1 = select(a = expand_dims_0, b = sequence, cond = var_198)[name = tensor("input_1")]; + tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208508736)))]; + tensor input_3 = linear(bias = linear_0_bias_0, weight = input_linear_weight, x = input_1)[name = tensor("linear_0")]; + tensor var_204 = const()[name = tensor("op_204"), val = tensor(0x1.4f8b58p-17)]; + tensor x_1_axes_0 = const()[name = tensor("x_1_axes_0"), val = tensor([-1])]; + tensor x_1 = layer_norm(axes = x_1_axes_0, beta = norm0_1_bias, epsilon = var_204, gamma = norm0_1_weight, x = input_3)[name = tensor("x_1")]; + tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208512896)))]; + tensor var_236 = linear(bias = linear_1_bias_0, weight = attn0_in_proj_weight, x = x_1)[name = tensor("linear_1")]; + tensor var_240 = const()[name = tensor("op_240"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_1 = reshape(shape = var_240, x = var_236)[name = tensor("qkv_1")]; + tensor q_1_begin_0 = const()[name = tensor("q_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_1_end_0 = const()[name = tensor("q_1_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_1_end_mask_0 = const()[name = tensor("q_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_1_squeeze_mask_0 = const()[name = tensor("q_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_1 = slice_by_index(begin = q_1_begin_0, end = q_1_end_0, end_mask = q_1_end_mask_0, squeeze_mask = q_1_squeeze_mask_0, x = qkv_1)[name = tensor("q_1")]; + tensor k_1_begin_0 = const()[name = tensor("k_1_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_1_end_0 = const()[name = tensor("k_1_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_1_end_mask_0 = const()[name = tensor("k_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_1_squeeze_mask_0 = const()[name = tensor("k_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_1 = slice_by_index(begin = k_1_begin_0, end = k_1_end_0, end_mask = k_1_end_mask_0, squeeze_mask = k_1_squeeze_mask_0, x = qkv_1)[name = tensor("k_1")]; + tensor v_1_begin_0 = const()[name = tensor("v_1_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_1_end_0 = const()[name = tensor("v_1_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_1_end_mask_0 = const()[name = tensor("v_1_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_1_squeeze_mask_0 = const()[name = tensor("v_1_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_1 = slice_by_index(begin = v_1_begin_0, end = v_1_end_0, end_mask = v_1_end_mask_0, squeeze_mask = v_1_squeeze_mask_0, x = qkv_1)[name = tensor("v_1")]; + tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208525248)))]; + tensor var_344 = const()[name = tensor("op_344"), val = tensor([1, 1, 1, 1])]; + tensor ts_5 = reshape(shape = var_344, x = position0)[name = tensor("ts_5")]; + tensor var_348 = const()[name = tensor("op_348"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_1 = reshape(shape = var_348, x = q_1)[name = tensor("q_complex_1")]; + tensor var_352 = const()[name = tensor("op_352"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_1 = reshape(shape = var_352, x = k_1)[name = tensor("k_complex_1")]; + tensor var_356_begin_0 = const()[name = tensor("op_356_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_356_end_0 = const()[name = tensor("op_356_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_356_end_mask_0 = const()[name = tensor("op_356_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_356_squeeze_mask_0 = const()[name = tensor("op_356_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_356 = slice_by_index(begin = var_356_begin_0, end = var_356_end_0, end_mask = var_356_end_mask_0, squeeze_mask = var_356_squeeze_mask_0, x = q_complex_1)[name = tensor("op_356")]; + tensor var_364_begin_0 = const()[name = tensor("op_364_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_364_end_0 = const()[name = tensor("op_364_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_364_end_mask_0 = const()[name = tensor("op_364_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_364_squeeze_mask_0 = const()[name = tensor("op_364_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_364 = slice_by_index(begin = var_364_begin_0, end = var_364_end_0, end_mask = var_364_end_mask_0, squeeze_mask = var_364_squeeze_mask_0, x = q_complex_1)[name = tensor("op_364")]; + tensor var_372_begin_0 = const()[name = tensor("op_372_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_372_end_0 = const()[name = tensor("op_372_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_372_end_mask_0 = const()[name = tensor("op_372_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_372_squeeze_mask_0 = const()[name = tensor("op_372_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_372 = slice_by_index(begin = var_372_begin_0, end = var_372_end_0, end_mask = var_372_end_mask_0, squeeze_mask = var_372_squeeze_mask_0, x = k_complex_1)[name = tensor("op_372")]; + tensor var_380_begin_0 = const()[name = tensor("op_380_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_380_end_0 = const()[name = tensor("op_380_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_380_end_mask_0 = const()[name = tensor("op_380_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_380_squeeze_mask_0 = const()[name = tensor("op_380_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_380 = slice_by_index(begin = var_380_begin_0, end = var_380_end_0, end_mask = var_380_end_mask_0, squeeze_mask = var_380_squeeze_mask_0, x = k_complex_1)[name = tensor("op_380")]; + tensor var_386 = mul(x = freqs_1, y = ts_5)[name = tensor("op_386")]; + tensor rotr_1 = cos(x = var_386)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_386)[name = tensor("roti_1")]; + tensor var_390 = mul(x = var_356, y = rotr_1)[name = tensor("op_390")]; + tensor var_391 = mul(x = var_364, y = roti_1)[name = tensor("op_391")]; + tensor qor_1 = sub(x = var_390, y = var_391)[name = tensor("qor_1")]; + tensor var_394 = mul(x = var_356, y = roti_1)[name = tensor("op_394")]; + tensor var_395 = mul(x = var_364, y = rotr_1)[name = tensor("op_395")]; + tensor qoi_1 = add(x = var_394, y = var_395)[name = tensor("qoi_1")]; + tensor var_398 = mul(x = var_372, y = rotr_1)[name = tensor("op_398")]; + tensor var_399 = mul(x = var_380, y = roti_1)[name = tensor("op_399")]; + tensor kor_1 = sub(x = var_398, y = var_399)[name = tensor("kor_1")]; + tensor var_402 = mul(x = var_372, y = roti_1)[name = tensor("op_402")]; + tensor var_403 = mul(x = var_380, y = rotr_1)[name = tensor("op_403")]; + tensor koi_1 = add(x = var_402, y = var_403)[name = tensor("koi_1")]; + tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; + tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; + tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; + tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; + tensor var_432 = const()[name = tensor("op_432"), val = tensor([1, 1, 16, 64])]; + tensor q_3 = reshape(shape = var_432, x = qo_1)[name = tensor("q_3")]; + tensor var_434 = const()[name = tensor("op_434"), val = tensor([1, 1, 16, 64])]; + tensor k_3 = reshape(shape = var_434, x = ko_1)[name = tensor("k_3")]; + tensor _inversed_456_y_0 = const()[name = tensor("_inversed_456_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_456 = mul(x = ts_5, y = _inversed_456_y_0)[name = tensor("_inversed_456")]; + tensor var_457 = floor(x = _inversed_456)[name = tensor("op_457")]; + tensor var_458 = const()[name = tensor("op_458"), val = tensor(0x1p+9)]; + tensor var_459 = mul(x = var_457, y = var_458)[name = tensor("op_459")]; + tensor write_indices_float_3 = sub(x = ts_5, y = var_459)[name = tensor("write_indices_float_3")]; + tensor var_466_dtype_0 = const()[name = tensor("op_466_dtype_0"), val = tensor("int32")]; + tensor write_indices_1_reps_0 = const()[name = tensor("write_indices_1_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_466 = cast(dtype = var_466_dtype_0, x = write_indices_float_3)[name = tensor("cast_455")]; + tensor write_indices_1 = tile(reps = write_indices_1_reps_0, x = var_466)[name = tensor("write_indices_1")]; + tensor var_474_begin_0 = const()[name = tensor("op_474_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_474_end_0 = const()[name = tensor("op_474_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_474_end_mask_0 = const()[name = tensor("op_474_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_474_squeeze_mask_0 = const()[name = tensor("op_474_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_474 = slice_by_index(begin = var_474_begin_0, end = var_474_end_0, end_mask = var_474_end_mask_0, squeeze_mask = var_474_squeeze_mask_0, x = cache0)[name = tensor("op_474")]; + tensor var_476_axis_0 = const()[name = tensor("op_476_axis_0"), val = tensor(1)]; + tensor var_476_mode_0 = const()[name = tensor("op_476_mode_0"), val = tensor("update")]; + tensor var_476_validate_indices_0 = const()[name = tensor("op_476_validate_indices_0"), val = tensor(false)]; + tensor var_476 = scatter_along_axis(axis = var_476_axis_0, data = var_474, indices = write_indices_1, mode = var_476_mode_0, updates = k_3, validate_indices = var_476_validate_indices_0)[name = tensor("op_476")]; + tensor concat_2 = const()[name = tensor("concat_2"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_3 = const()[name = tensor("concat_3"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_48 = const()[name = tensor("shape_48"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_0 = const()[name = tensor("reduce_prod_0"), val = tensor(1048576)]; + tensor range_1d_0_start_0 = const()[name = tensor("range_1d_0_start_0"), val = tensor(0)]; + tensor range_1d_0_step_0 = const()[name = tensor("range_1d_0_step_0"), val = tensor(1)]; + tensor range_1d_0 = range_1d(end = reduce_prod_0, start = range_1d_0_start_0, step = range_1d_0_step_0)[name = tensor("range_1d_0")]; + tensor reshape_0 = reshape(shape = shape_48, x = range_1d_0)[name = tensor("reshape_0")]; + tensor slice_by_index_0 = slice_by_index(begin = concat_2, begin_mask = new_cache_1_internal_tensor_assign_1_begin_mask_0, end = concat_3, end_mask = new_cache_1_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_1_stride_0, x = reshape_0)[name = tensor("slice_by_index_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([-1])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = slice_by_index_0)[name = tensor("reshape_1")]; + tensor reshape_2_shape_0 = const()[name = tensor("reshape_2_shape_0"), val = tensor([-1])]; + tensor reshape_2 = reshape(shape = reshape_2_shape_0, x = var_476)[name = tensor("reshape_2")]; + tensor reshape_3_shape_0 = const()[name = tensor("reshape_3_shape_0"), val = tensor([-1])]; + tensor reshape_3 = reshape(shape = reshape_3_shape_0, x = cache0)[name = tensor("reshape_3")]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor scatter_0_validate_indices_0 = const()[name = tensor("scatter_0_validate_indices_0"), val = tensor(false)]; + tensor scatter_0 = scatter(axis = scatter_0_axis_0, data = reshape_3, indices = reshape_1, mode = scatter_0_mode_0, updates = reshape_2, validate_indices = scatter_0_validate_indices_0)[name = tensor("scatter_0")]; + tensor reshape_4 = reshape(shape = shape_48, x = scatter_0)[name = tensor("reshape_4")]; + tensor var_484_begin_0 = const()[name = tensor("op_484_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_484_end_0 = const()[name = tensor("op_484_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_484_end_mask_0 = const()[name = tensor("op_484_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_484_squeeze_mask_0 = const()[name = tensor("op_484_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_484 = slice_by_index(begin = var_484_begin_0, end = var_484_end_0, end_mask = var_484_end_mask_0, squeeze_mask = var_484_squeeze_mask_0, x = reshape_4)[name = tensor("op_484")]; + tensor var_486_axis_0 = const()[name = tensor("op_486_axis_0"), val = tensor(1)]; + tensor var_486_mode_0 = const()[name = tensor("op_486_mode_0"), val = tensor("update")]; + tensor var_486_validate_indices_0 = const()[name = tensor("op_486_validate_indices_0"), val = tensor(false)]; + tensor var_486 = scatter_along_axis(axis = var_486_axis_0, data = var_484, indices = write_indices_1, mode = var_486_mode_0, updates = v_1, validate_indices = var_486_validate_indices_0)[name = tensor("op_486")]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_5 = const()[name = tensor("concat_5"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_1_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_1_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_1_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_1_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_49 = const()[name = tensor("shape_49"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_1 = const()[name = tensor("reduce_prod_1"), val = tensor(1048576)]; + tensor range_1d_1_start_0 = const()[name = tensor("range_1d_1_start_0"), val = tensor(0)]; + tensor range_1d_1_step_0 = const()[name = tensor("range_1d_1_step_0"), val = tensor(1)]; + tensor range_1d_1 = range_1d(end = reduce_prod_1, start = range_1d_1_start_0, step = range_1d_1_step_0)[name = tensor("range_1d_1")]; + tensor reshape_5 = reshape(shape = shape_49, x = range_1d_1)[name = tensor("reshape_5")]; + tensor slice_by_index_1 = slice_by_index(begin = concat_4, begin_mask = new_cache_1_internal_tensor_assign_2_begin_mask_0, end = concat_5, end_mask = new_cache_1_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_1_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_1_internal_tensor_assign_2_stride_0, x = reshape_5)[name = tensor("slice_by_index_1")]; + tensor reshape_6_shape_0 = const()[name = tensor("reshape_6_shape_0"), val = tensor([-1])]; + tensor reshape_6 = reshape(shape = reshape_6_shape_0, x = slice_by_index_1)[name = tensor("reshape_6")]; + tensor reshape_7_shape_0 = const()[name = tensor("reshape_7_shape_0"), val = tensor([-1])]; + tensor reshape_7 = reshape(shape = reshape_7_shape_0, x = var_486)[name = tensor("reshape_7")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([-1])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = reshape_4)[name = tensor("reshape_8")]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor scatter_1_validate_indices_0 = const()[name = tensor("scatter_1_validate_indices_0"), val = tensor(false)]; + tensor scatter_1 = scatter(axis = scatter_1_axis_0, data = reshape_8, indices = reshape_6, mode = scatter_1_mode_0, updates = reshape_7, validate_indices = scatter_1_validate_indices_0)[name = tensor("scatter_1")]; + tensor new_cache_1_internal_tensor_assign_2 = reshape(shape = shape_49, x = scatter_1)[name = tensor("reshape_9")]; + tensor keys_1_begin_0 = const()[name = tensor("keys_1_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_1_end_0 = const()[name = tensor("keys_1_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_1_end_mask_0 = const()[name = tensor("keys_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_1_squeeze_mask_0 = const()[name = tensor("keys_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_1 = slice_by_index(begin = keys_1_begin_0, end = keys_1_end_0, end_mask = keys_1_end_mask_0, squeeze_mask = keys_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("keys_1")]; + tensor values_1_begin_0 = const()[name = tensor("values_1_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_1_end_0 = const()[name = tensor("values_1_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_1_end_mask_0 = const()[name = tensor("values_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_1_squeeze_mask_0 = const()[name = tensor("values_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_1 = slice_by_index(begin = values_1_begin_0, end = values_1_end_0, end_mask = values_1_end_mask_0, squeeze_mask = values_1_squeeze_mask_0, x = new_cache_1_internal_tensor_assign_2)[name = tensor("values_1")]; + tensor var_498 = not_equal(x = keys_1, y = keys_1)[name = tensor("op_498")]; + tensor var_504 = const()[name = tensor("op_504"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208525440)))]; + tensor keys_3 = select(a = var_504, b = keys_1, cond = var_498)[name = tensor("keys_3")]; + tensor var_506 = not_equal(x = values_1, y = values_1)[name = tensor("op_506")]; + tensor values_3 = select(a = var_504, b = values_1, cond = var_506)[name = tensor("values_3")]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = tensor("op_543"), val = tensor([1, 1, 1])]; + tensor var_544 = reshape(shape = var_543, x = position0)[name = tensor("op_544")]; + tensor var_561 = const()[name = tensor("op_561"), val = tensor(0x1p+0)]; + tensor valid_len_1 = add(x = var_544, y = var_561)[name = tensor("valid_len_1")]; + tensor k_positions_1_promoted = const()[name = tensor("k_positions_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210622656)))]; + tensor valid_mask_1 = less(x = k_positions_1_promoted, y = valid_len_1)[name = tensor("valid_mask_1")]; + tensor causal_mask_1 = less_equal(x = k_positions_1_promoted, y = var_544)[name = tensor("causal_mask_1")]; + tensor attn_mask_1 = logical_and(x = valid_mask_1, y = causal_mask_1)[name = tensor("attn_mask_1")]; + tensor attn_mask_3_axes_0 = const()[name = tensor("attn_mask_3_axes_0"), val = tensor([1])]; + tensor attn_mask_3 = expand_dims(axes = attn_mask_3_axes_0, x = attn_mask_1)[name = tensor("attn_mask_3")]; + tensor var_573 = const()[name = tensor("op_573"), val = tensor([0x1.fffe5cp-4])]; + tensor var_579_transpose_x_0 = const()[name = tensor("op_579_transpose_x_0"), val = tensor(false)]; + tensor var_579_transpose_y_0 = const()[name = tensor("op_579_transpose_y_0"), val = tensor(false)]; + tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = keys_3)[name = tensor("transpose_213")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = q_3)[name = tensor("transpose_214")]; + tensor var_579 = matmul(transpose_x = var_579_transpose_x_0, transpose_y = var_579_transpose_y_0, x = transpose_72, y = transpose_73)[name = tensor("op_579")]; + tensor attn_weights_1 = mul(x = var_579, y = var_573)[name = tensor("attn_weights_1")]; + tensor var_581 = logical_not(x = attn_mask_3)[name = tensor("op_581")]; + tensor var_582 = const()[name = tensor("op_582"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_3 = select(a = var_582, b = attn_weights_1, cond = var_581)[name = tensor("attn_weights_3")]; + tensor var_584 = const()[name = tensor("op_584"), val = tensor(-1)]; + tensor attn_weights_5 = softmax(axis = var_584, x = attn_weights_3)[name = tensor("attn_weights_5")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor values_5 = transpose(perm = var_530, x = values_3)[name = tensor("transpose_215")]; + tensor attn_output_1 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = attn_weights_5, y = values_5)[name = tensor("attn_output_1")]; + tensor var_592 = const()[name = tensor("op_592"), val = tensor([0, 2, 1, 3])]; + tensor var_595 = const()[name = tensor("op_595"), val = tensor([1, 1, 1024])]; + tensor var_593 = transpose(perm = var_592, x = attn_output_1)[name = tensor("transpose_212")]; + tensor input_5 = reshape(shape = var_595, x = var_593)[name = tensor("input_5")]; + tensor attn_out_1 = linear(bias = linear_0_bias_0, weight = attn0_out_proj_weight, x = input_5)[name = tensor("linear_2")]; + tensor var_601 = const()[name = tensor("op_601"), val = tensor(0x1p+0)]; + tensor var_602 = add(x = position0, y = var_601)[name = tensor("op_602")]; + tensor input_7 = add(x = input_3, y = attn_out_1)[name = tensor("input_7")]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor(0x1.4f8b58p-17)]; + tensor input_9_axes_0 = const()[name = tensor("input_9_axes_0"), val = tensor([-1])]; + tensor input_9 = layer_norm(axes = input_9_axes_0, beta = norm0_2_bias, epsilon = var_606, gamma = norm0_2_weight, x = input_7)[name = tensor("input_9")]; + tensor linear_3_bias_0 = const()[name = tensor("linear_3_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210624768)))]; + tensor var_614 = linear(bias = linear_3_bias_0, weight = linear0_1_weight, x = input_9)[name = tensor("linear_3")]; + tensor input_11_mode_0 = const()[name = tensor("input_11_mode_0"), val = tensor("EXACT")]; + tensor input_11 = gelu(mode = input_11_mode_0, x = var_614)[name = tensor("input_11")]; + tensor ffn_out_1 = linear(bias = linear_0_bias_0, weight = linear0_2_weight, x = input_11)[name = tensor("linear_4")]; + tensor input_13 = add(x = input_7, y = ffn_out_1)[name = tensor("input_13")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor(0x1.4f8b58p-17)]; + tensor x_3_axes_0 = const()[name = tensor("x_3_axes_0"), val = tensor([-1])]; + tensor x_3 = layer_norm(axes = x_3_axes_0, beta = norm1_1_bias, epsilon = var_623, gamma = norm1_1_weight, x = input_13)[name = tensor("x_3")]; + tensor var_655 = linear(bias = linear_1_bias_0, weight = attn1_in_proj_weight, x = x_3)[name = tensor("linear_5")]; + tensor var_659 = const()[name = tensor("op_659"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_3 = reshape(shape = var_659, x = var_655)[name = tensor("qkv_3")]; + tensor q_7_begin_0 = const()[name = tensor("q_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_7_end_0 = const()[name = tensor("q_7_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_7_end_mask_0 = const()[name = tensor("q_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_7_squeeze_mask_0 = const()[name = tensor("q_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_7 = slice_by_index(begin = q_7_begin_0, end = q_7_end_0, end_mask = q_7_end_mask_0, squeeze_mask = q_7_squeeze_mask_0, x = qkv_3)[name = tensor("q_7")]; + tensor k_5_begin_0 = const()[name = tensor("k_5_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_5_end_0 = const()[name = tensor("k_5_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_5_end_mask_0 = const()[name = tensor("k_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_5_squeeze_mask_0 = const()[name = tensor("k_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_5 = slice_by_index(begin = k_5_begin_0, end = k_5_end_0, end_mask = k_5_end_mask_0, squeeze_mask = k_5_squeeze_mask_0, x = qkv_3)[name = tensor("k_5")]; + tensor v_3_begin_0 = const()[name = tensor("v_3_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_3_end_0 = const()[name = tensor("v_3_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_3_end_mask_0 = const()[name = tensor("v_3_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_3_squeeze_mask_0 = const()[name = tensor("v_3_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_3 = slice_by_index(begin = v_3_begin_0, end = v_3_end_0, end_mask = v_3_end_mask_0, squeeze_mask = v_3_squeeze_mask_0, x = qkv_3)[name = tensor("v_3")]; + tensor freqs_3 = const()[name = tensor("freqs_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210641216)))]; + tensor var_763 = const()[name = tensor("op_763"), val = tensor([1, 1, 1, 1])]; + tensor ts_11 = reshape(shape = var_763, x = position1)[name = tensor("ts_11")]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_3 = reshape(shape = var_767, x = q_7)[name = tensor("q_complex_3")]; + tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_3 = reshape(shape = var_771, x = k_5)[name = tensor("k_complex_3")]; + tensor var_775_begin_0 = const()[name = tensor("op_775_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_775_end_0 = const()[name = tensor("op_775_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_775_end_mask_0 = const()[name = tensor("op_775_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_775_squeeze_mask_0 = const()[name = tensor("op_775_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_775 = slice_by_index(begin = var_775_begin_0, end = var_775_end_0, end_mask = var_775_end_mask_0, squeeze_mask = var_775_squeeze_mask_0, x = q_complex_3)[name = tensor("op_775")]; + tensor var_783_begin_0 = const()[name = tensor("op_783_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_783_end_0 = const()[name = tensor("op_783_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_783_end_mask_0 = const()[name = tensor("op_783_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_783_squeeze_mask_0 = const()[name = tensor("op_783_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_783 = slice_by_index(begin = var_783_begin_0, end = var_783_end_0, end_mask = var_783_end_mask_0, squeeze_mask = var_783_squeeze_mask_0, x = q_complex_3)[name = tensor("op_783")]; + tensor var_791_begin_0 = const()[name = tensor("op_791_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_791_end_0 = const()[name = tensor("op_791_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_791_end_mask_0 = const()[name = tensor("op_791_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_791_squeeze_mask_0 = const()[name = tensor("op_791_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_791 = slice_by_index(begin = var_791_begin_0, end = var_791_end_0, end_mask = var_791_end_mask_0, squeeze_mask = var_791_squeeze_mask_0, x = k_complex_3)[name = tensor("op_791")]; + tensor var_799_begin_0 = const()[name = tensor("op_799_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_799_end_0 = const()[name = tensor("op_799_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_799_end_mask_0 = const()[name = tensor("op_799_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_799_squeeze_mask_0 = const()[name = tensor("op_799_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_799 = slice_by_index(begin = var_799_begin_0, end = var_799_end_0, end_mask = var_799_end_mask_0, squeeze_mask = var_799_squeeze_mask_0, x = k_complex_3)[name = tensor("op_799")]; + tensor var_805 = mul(x = freqs_3, y = ts_11)[name = tensor("op_805")]; + tensor rotr_3 = cos(x = var_805)[name = tensor("rotr_3")]; + tensor roti_3 = sin(x = var_805)[name = tensor("roti_3")]; + tensor var_809 = mul(x = var_775, y = rotr_3)[name = tensor("op_809")]; + tensor var_810 = mul(x = var_783, y = roti_3)[name = tensor("op_810")]; + tensor qor_5 = sub(x = var_809, y = var_810)[name = tensor("qor_5")]; + tensor var_813 = mul(x = var_775, y = roti_3)[name = tensor("op_813")]; + tensor var_814 = mul(x = var_783, y = rotr_3)[name = tensor("op_814")]; + tensor qoi_5 = add(x = var_813, y = var_814)[name = tensor("qoi_5")]; + tensor var_817 = mul(x = var_791, y = rotr_3)[name = tensor("op_817")]; + tensor var_818 = mul(x = var_799, y = roti_3)[name = tensor("op_818")]; + tensor kor_5 = sub(x = var_817, y = var_818)[name = tensor("kor_5")]; + tensor var_821 = mul(x = var_791, y = roti_3)[name = tensor("op_821")]; + tensor var_822 = mul(x = var_799, y = rotr_3)[name = tensor("op_822")]; + tensor koi_5 = add(x = var_821, y = var_822)[name = tensor("koi_5")]; + tensor qo_3_axis_0 = const()[name = tensor("qo_3_axis_0"), val = tensor(-1)]; + tensor qo_3 = stack(axis = qo_3_axis_0, values = (qor_5, qoi_5))[name = tensor("qo_3")]; + tensor ko_3_axis_0 = const()[name = tensor("ko_3_axis_0"), val = tensor(-1)]; + tensor ko_3 = stack(axis = ko_3_axis_0, values = (kor_5, koi_5))[name = tensor("ko_3")]; + tensor var_851 = const()[name = tensor("op_851"), val = tensor([1, 1, 16, 64])]; + tensor q_9 = reshape(shape = var_851, x = qo_3)[name = tensor("q_9")]; + tensor var_853 = const()[name = tensor("op_853"), val = tensor([1, 1, 16, 64])]; + tensor k_7 = reshape(shape = var_853, x = ko_3)[name = tensor("k_7")]; + tensor _inversed_875_y_0 = const()[name = tensor("_inversed_875_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_875 = mul(x = ts_11, y = _inversed_875_y_0)[name = tensor("_inversed_875")]; + tensor var_876 = floor(x = _inversed_875)[name = tensor("op_876")]; + tensor var_877 = const()[name = tensor("op_877"), val = tensor(0x1p+9)]; + tensor var_878 = mul(x = var_876, y = var_877)[name = tensor("op_878")]; + tensor write_indices_float_7 = sub(x = ts_11, y = var_878)[name = tensor("write_indices_float_7")]; + tensor var_885_dtype_0 = const()[name = tensor("op_885_dtype_0"), val = tensor("int32")]; + tensor write_indices_3_reps_0 = const()[name = tensor("write_indices_3_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_885 = cast(dtype = var_885_dtype_0, x = write_indices_float_7)[name = tensor("cast_454")]; + tensor write_indices_3 = tile(reps = write_indices_3_reps_0, x = var_885)[name = tensor("write_indices_3")]; + tensor var_893_begin_0 = const()[name = tensor("op_893_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_893_end_0 = const()[name = tensor("op_893_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_893_end_mask_0 = const()[name = tensor("op_893_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_893_squeeze_mask_0 = const()[name = tensor("op_893_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_893 = slice_by_index(begin = var_893_begin_0, end = var_893_end_0, end_mask = var_893_end_mask_0, squeeze_mask = var_893_squeeze_mask_0, x = cache1)[name = tensor("op_893")]; + tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(1)]; + tensor var_895_mode_0 = const()[name = tensor("op_895_mode_0"), val = tensor("update")]; + tensor var_895_validate_indices_0 = const()[name = tensor("op_895_validate_indices_0"), val = tensor(false)]; + tensor var_895 = scatter_along_axis(axis = var_895_axis_0, data = var_893, indices = write_indices_3, mode = var_895_mode_0, updates = k_7, validate_indices = var_895_validate_indices_0)[name = tensor("op_895")]; + tensor concat_9 = const()[name = tensor("concat_9"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_10 = const()[name = tensor("concat_10"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_50 = const()[name = tensor("shape_50"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_2 = const()[name = tensor("reduce_prod_2"), val = tensor(1048576)]; + tensor range_1d_2_start_0 = const()[name = tensor("range_1d_2_start_0"), val = tensor(0)]; + tensor range_1d_2_step_0 = const()[name = tensor("range_1d_2_step_0"), val = tensor(1)]; + tensor range_1d_2 = range_1d(end = reduce_prod_2, start = range_1d_2_start_0, step = range_1d_2_step_0)[name = tensor("range_1d_2")]; + tensor reshape_10 = reshape(shape = shape_50, x = range_1d_2)[name = tensor("reshape_10")]; + tensor slice_by_index_2 = slice_by_index(begin = concat_9, begin_mask = new_cache_3_internal_tensor_assign_1_begin_mask_0, end = concat_10, end_mask = new_cache_3_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_1_stride_0, x = reshape_10)[name = tensor("slice_by_index_2")]; + tensor reshape_11_shape_0 = const()[name = tensor("reshape_11_shape_0"), val = tensor([-1])]; + tensor reshape_11 = reshape(shape = reshape_11_shape_0, x = slice_by_index_2)[name = tensor("reshape_11")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([-1])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = var_895)[name = tensor("reshape_12")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([-1])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = cache1)[name = tensor("reshape_13")]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2_validate_indices_0 = const()[name = tensor("scatter_2_validate_indices_0"), val = tensor(false)]; + tensor scatter_2 = scatter(axis = scatter_2_axis_0, data = reshape_13, indices = reshape_11, mode = scatter_2_mode_0, updates = reshape_12, validate_indices = scatter_2_validate_indices_0)[name = tensor("scatter_2")]; + tensor reshape_14 = reshape(shape = shape_50, x = scatter_2)[name = tensor("reshape_14")]; + tensor var_903_begin_0 = const()[name = tensor("op_903_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_903_end_0 = const()[name = tensor("op_903_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_903_end_mask_0 = const()[name = tensor("op_903_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_903_squeeze_mask_0 = const()[name = tensor("op_903_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_903 = slice_by_index(begin = var_903_begin_0, end = var_903_end_0, end_mask = var_903_end_mask_0, squeeze_mask = var_903_squeeze_mask_0, x = reshape_14)[name = tensor("op_903")]; + tensor var_905_axis_0 = const()[name = tensor("op_905_axis_0"), val = tensor(1)]; + tensor var_905_mode_0 = const()[name = tensor("op_905_mode_0"), val = tensor("update")]; + tensor var_905_validate_indices_0 = const()[name = tensor("op_905_validate_indices_0"), val = tensor(false)]; + tensor var_905 = scatter_along_axis(axis = var_905_axis_0, data = var_903, indices = write_indices_3, mode = var_905_mode_0, updates = v_3, validate_indices = var_905_validate_indices_0)[name = tensor("op_905")]; + tensor concat_11 = const()[name = tensor("concat_11"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_12 = const()[name = tensor("concat_12"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_3_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_3_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_3_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_3_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_51 = const()[name = tensor("shape_51"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_3 = const()[name = tensor("reduce_prod_3"), val = tensor(1048576)]; + tensor range_1d_3_start_0 = const()[name = tensor("range_1d_3_start_0"), val = tensor(0)]; + tensor range_1d_3_step_0 = const()[name = tensor("range_1d_3_step_0"), val = tensor(1)]; + tensor range_1d_3 = range_1d(end = reduce_prod_3, start = range_1d_3_start_0, step = range_1d_3_step_0)[name = tensor("range_1d_3")]; + tensor reshape_15 = reshape(shape = shape_51, x = range_1d_3)[name = tensor("reshape_15")]; + tensor slice_by_index_3 = slice_by_index(begin = concat_11, begin_mask = new_cache_3_internal_tensor_assign_2_begin_mask_0, end = concat_12, end_mask = new_cache_3_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_3_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_3_internal_tensor_assign_2_stride_0, x = reshape_15)[name = tensor("slice_by_index_3")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([-1])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = slice_by_index_3)[name = tensor("reshape_16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([-1])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = var_905)[name = tensor("reshape_17")]; + tensor reshape_18_shape_0 = const()[name = tensor("reshape_18_shape_0"), val = tensor([-1])]; + tensor reshape_18 = reshape(shape = reshape_18_shape_0, x = reshape_14)[name = tensor("reshape_18")]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3_validate_indices_0 = const()[name = tensor("scatter_3_validate_indices_0"), val = tensor(false)]; + tensor scatter_3 = scatter(axis = scatter_3_axis_0, data = reshape_18, indices = reshape_16, mode = scatter_3_mode_0, updates = reshape_17, validate_indices = scatter_3_validate_indices_0)[name = tensor("scatter_3")]; + tensor new_cache_3_internal_tensor_assign_2 = reshape(shape = shape_51, x = scatter_3)[name = tensor("reshape_19")]; + tensor keys_7_begin_0 = const()[name = tensor("keys_7_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_7_end_0 = const()[name = tensor("keys_7_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_7_end_mask_0 = const()[name = tensor("keys_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_7_squeeze_mask_0 = const()[name = tensor("keys_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_7 = slice_by_index(begin = keys_7_begin_0, end = keys_7_end_0, end_mask = keys_7_end_mask_0, squeeze_mask = keys_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("keys_7")]; + tensor values_7_begin_0 = const()[name = tensor("values_7_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_7_end_0 = const()[name = tensor("values_7_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_7_end_mask_0 = const()[name = tensor("values_7_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_7_squeeze_mask_0 = const()[name = tensor("values_7_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_7 = slice_by_index(begin = values_7_begin_0, end = values_7_end_0, end_mask = values_7_end_mask_0, squeeze_mask = values_7_squeeze_mask_0, x = new_cache_3_internal_tensor_assign_2)[name = tensor("values_7")]; + tensor var_917 = not_equal(x = keys_7, y = keys_7)[name = tensor("op_917")]; + tensor keys_9 = select(a = var_504, b = keys_7, cond = var_917)[name = tensor("keys_9")]; + tensor var_925 = not_equal(x = values_7, y = values_7)[name = tensor("op_925")]; + tensor values_9 = select(a = var_504, b = values_7, cond = var_925)[name = tensor("values_9")]; + tensor var_949 = const()[name = tensor("op_949"), val = tensor([0, 2, 1, 3])]; + tensor var_962 = const()[name = tensor("op_962"), val = tensor([1, 1, 1])]; + tensor var_963 = reshape(shape = var_962, x = position1)[name = tensor("op_963")]; + tensor var_980 = const()[name = tensor("op_980"), val = tensor(0x1p+0)]; + tensor valid_len_3 = add(x = var_963, y = var_980)[name = tensor("valid_len_3")]; + tensor valid_mask_3 = less(x = k_positions_1_promoted, y = valid_len_3)[name = tensor("valid_mask_3")]; + tensor causal_mask_3 = less_equal(x = k_positions_1_promoted, y = var_963)[name = tensor("causal_mask_3")]; + tensor attn_mask_5 = logical_and(x = valid_mask_3, y = causal_mask_3)[name = tensor("attn_mask_5")]; + tensor attn_mask_7_axes_0 = const()[name = tensor("attn_mask_7_axes_0"), val = tensor([1])]; + tensor attn_mask_7 = expand_dims(axes = attn_mask_7_axes_0, x = attn_mask_5)[name = tensor("attn_mask_7")]; + tensor var_992 = const()[name = tensor("op_992"), val = tensor([0x1.fffe5cp-4])]; + tensor var_998_transpose_x_0 = const()[name = tensor("op_998_transpose_x_0"), val = tensor(false)]; + tensor var_998_transpose_y_0 = const()[name = tensor("op_998_transpose_y_0"), val = tensor(false)]; + tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = keys_9)[name = tensor("transpose_209")]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = q_9)[name = tensor("transpose_210")]; + tensor var_998 = matmul(transpose_x = var_998_transpose_x_0, transpose_y = var_998_transpose_y_0, x = transpose_74, y = transpose_75)[name = tensor("op_998")]; + tensor attn_weights_7 = mul(x = var_998, y = var_992)[name = tensor("attn_weights_7")]; + tensor var_1000 = logical_not(x = attn_mask_7)[name = tensor("op_1000")]; + tensor var_1001 = const()[name = tensor("op_1001"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_9 = select(a = var_1001, b = attn_weights_7, cond = var_1000)[name = tensor("attn_weights_9")]; + tensor var_1003 = const()[name = tensor("op_1003"), val = tensor(-1)]; + tensor attn_weights_11 = softmax(axis = var_1003, x = attn_weights_9)[name = tensor("attn_weights_11")]; + tensor attn_output_3_transpose_x_0 = const()[name = tensor("attn_output_3_transpose_x_0"), val = tensor(false)]; + tensor attn_output_3_transpose_y_0 = const()[name = tensor("attn_output_3_transpose_y_0"), val = tensor(false)]; + tensor values_11 = transpose(perm = var_949, x = values_9)[name = tensor("transpose_211")]; + tensor attn_output_3 = matmul(transpose_x = attn_output_3_transpose_x_0, transpose_y = attn_output_3_transpose_y_0, x = attn_weights_11, y = values_11)[name = tensor("attn_output_3")]; + tensor var_1011 = const()[name = tensor("op_1011"), val = tensor([0, 2, 1, 3])]; + tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1, 1024])]; + tensor var_1012 = transpose(perm = var_1011, x = attn_output_3)[name = tensor("transpose_208")]; + tensor input_15 = reshape(shape = var_1014, x = var_1012)[name = tensor("input_15")]; + tensor attn_out_3 = linear(bias = linear_0_bias_0, weight = attn1_out_proj_weight, x = input_15)[name = tensor("linear_6")]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor(0x1p+0)]; + tensor var_1021 = add(x = position1, y = var_1020)[name = tensor("op_1021")]; + tensor input_17 = add(x = input_13, y = attn_out_3)[name = tensor("input_17")]; + tensor var_1025 = const()[name = tensor("op_1025"), val = tensor(0x1.4f8b58p-17)]; + tensor input_19_axes_0 = const()[name = tensor("input_19_axes_0"), val = tensor([-1])]; + tensor input_19 = layer_norm(axes = input_19_axes_0, beta = norm1_2_bias, epsilon = var_1025, gamma = norm1_2_weight, x = input_17)[name = tensor("input_19")]; + tensor var_1033 = linear(bias = linear_3_bias_0, weight = linear1_1_weight, x = input_19)[name = tensor("linear_7")]; + tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("EXACT")]; + tensor input_21 = gelu(mode = input_21_mode_0, x = var_1033)[name = tensor("input_21")]; + tensor ffn_out_3 = linear(bias = linear_0_bias_0, weight = linear1_2_weight, x = input_21)[name = tensor("linear_8")]; + tensor input_23 = add(x = input_17, y = ffn_out_3)[name = tensor("input_23")]; + tensor var_1042 = const()[name = tensor("op_1042"), val = tensor(0x1.4f8b58p-17)]; + tensor x_5_axes_0 = const()[name = tensor("x_5_axes_0"), val = tensor([-1])]; + tensor x_5 = layer_norm(axes = x_5_axes_0, beta = norm2_1_bias, epsilon = var_1042, gamma = norm2_1_weight, x = input_23)[name = tensor("x_5")]; + tensor var_1074 = linear(bias = linear_1_bias_0, weight = attn2_in_proj_weight, x = x_5)[name = tensor("linear_9")]; + tensor var_1078 = const()[name = tensor("op_1078"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_5 = reshape(shape = var_1078, x = var_1074)[name = tensor("qkv_5")]; + tensor q_13_begin_0 = const()[name = tensor("q_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_13_end_0 = const()[name = tensor("q_13_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_13_end_mask_0 = const()[name = tensor("q_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_13_squeeze_mask_0 = const()[name = tensor("q_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_13 = slice_by_index(begin = q_13_begin_0, end = q_13_end_0, end_mask = q_13_end_mask_0, squeeze_mask = q_13_squeeze_mask_0, x = qkv_5)[name = tensor("q_13")]; + tensor k_9_begin_0 = const()[name = tensor("k_9_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_9_end_0 = const()[name = tensor("k_9_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_9_end_mask_0 = const()[name = tensor("k_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_9_squeeze_mask_0 = const()[name = tensor("k_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_9 = slice_by_index(begin = k_9_begin_0, end = k_9_end_0, end_mask = k_9_end_mask_0, squeeze_mask = k_9_squeeze_mask_0, x = qkv_5)[name = tensor("k_9")]; + tensor v_5_begin_0 = const()[name = tensor("v_5_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_5_end_0 = const()[name = tensor("v_5_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_5_end_mask_0 = const()[name = tensor("v_5_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_5_squeeze_mask_0 = const()[name = tensor("v_5_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_5 = slice_by_index(begin = v_5_begin_0, end = v_5_end_0, end_mask = v_5_end_mask_0, squeeze_mask = v_5_squeeze_mask_0, x = qkv_5)[name = tensor("v_5")]; + tensor freqs_5 = const()[name = tensor("freqs_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210641408)))]; + tensor var_1182 = const()[name = tensor("op_1182"), val = tensor([1, 1, 1, 1])]; + tensor ts_17 = reshape(shape = var_1182, x = position2)[name = tensor("ts_17")]; + tensor var_1186 = const()[name = tensor("op_1186"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_5 = reshape(shape = var_1186, x = q_13)[name = tensor("q_complex_5")]; + tensor var_1190 = const()[name = tensor("op_1190"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_5 = reshape(shape = var_1190, x = k_9)[name = tensor("k_complex_5")]; + tensor var_1194_begin_0 = const()[name = tensor("op_1194_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1194_end_0 = const()[name = tensor("op_1194_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1194_end_mask_0 = const()[name = tensor("op_1194_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1194_squeeze_mask_0 = const()[name = tensor("op_1194_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1194 = slice_by_index(begin = var_1194_begin_0, end = var_1194_end_0, end_mask = var_1194_end_mask_0, squeeze_mask = var_1194_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1194")]; + tensor var_1202_begin_0 = const()[name = tensor("op_1202_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1202_end_0 = const()[name = tensor("op_1202_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1202_end_mask_0 = const()[name = tensor("op_1202_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1202_squeeze_mask_0 = const()[name = tensor("op_1202_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1202 = slice_by_index(begin = var_1202_begin_0, end = var_1202_end_0, end_mask = var_1202_end_mask_0, squeeze_mask = var_1202_squeeze_mask_0, x = q_complex_5)[name = tensor("op_1202")]; + tensor var_1210_begin_0 = const()[name = tensor("op_1210_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1210_end_0 = const()[name = tensor("op_1210_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1210_end_mask_0 = const()[name = tensor("op_1210_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1210_squeeze_mask_0 = const()[name = tensor("op_1210_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1210 = slice_by_index(begin = var_1210_begin_0, end = var_1210_end_0, end_mask = var_1210_end_mask_0, squeeze_mask = var_1210_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1210")]; + tensor var_1218_begin_0 = const()[name = tensor("op_1218_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1218_end_0 = const()[name = tensor("op_1218_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1218_end_mask_0 = const()[name = tensor("op_1218_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1218_squeeze_mask_0 = const()[name = tensor("op_1218_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1218 = slice_by_index(begin = var_1218_begin_0, end = var_1218_end_0, end_mask = var_1218_end_mask_0, squeeze_mask = var_1218_squeeze_mask_0, x = k_complex_5)[name = tensor("op_1218")]; + tensor var_1224 = mul(x = freqs_5, y = ts_17)[name = tensor("op_1224")]; + tensor rotr_5 = cos(x = var_1224)[name = tensor("rotr_5")]; + tensor roti_5 = sin(x = var_1224)[name = tensor("roti_5")]; + tensor var_1228 = mul(x = var_1194, y = rotr_5)[name = tensor("op_1228")]; + tensor var_1229 = mul(x = var_1202, y = roti_5)[name = tensor("op_1229")]; + tensor qor_9 = sub(x = var_1228, y = var_1229)[name = tensor("qor_9")]; + tensor var_1232 = mul(x = var_1194, y = roti_5)[name = tensor("op_1232")]; + tensor var_1233 = mul(x = var_1202, y = rotr_5)[name = tensor("op_1233")]; + tensor qoi_9 = add(x = var_1232, y = var_1233)[name = tensor("qoi_9")]; + tensor var_1236 = mul(x = var_1210, y = rotr_5)[name = tensor("op_1236")]; + tensor var_1237 = mul(x = var_1218, y = roti_5)[name = tensor("op_1237")]; + tensor kor_9 = sub(x = var_1236, y = var_1237)[name = tensor("kor_9")]; + tensor var_1240 = mul(x = var_1210, y = roti_5)[name = tensor("op_1240")]; + tensor var_1241 = mul(x = var_1218, y = rotr_5)[name = tensor("op_1241")]; + tensor koi_9 = add(x = var_1240, y = var_1241)[name = tensor("koi_9")]; + tensor qo_5_axis_0 = const()[name = tensor("qo_5_axis_0"), val = tensor(-1)]; + tensor qo_5 = stack(axis = qo_5_axis_0, values = (qor_9, qoi_9))[name = tensor("qo_5")]; + tensor ko_5_axis_0 = const()[name = tensor("ko_5_axis_0"), val = tensor(-1)]; + tensor ko_5 = stack(axis = ko_5_axis_0, values = (kor_9, koi_9))[name = tensor("ko_5")]; + tensor var_1270 = const()[name = tensor("op_1270"), val = tensor([1, 1, 16, 64])]; + tensor q_15 = reshape(shape = var_1270, x = qo_5)[name = tensor("q_15")]; + tensor var_1272 = const()[name = tensor("op_1272"), val = tensor([1, 1, 16, 64])]; + tensor k_11 = reshape(shape = var_1272, x = ko_5)[name = tensor("k_11")]; + tensor _inversed_1294_y_0 = const()[name = tensor("_inversed_1294_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1294 = mul(x = ts_17, y = _inversed_1294_y_0)[name = tensor("_inversed_1294")]; + tensor var_1295 = floor(x = _inversed_1294)[name = tensor("op_1295")]; + tensor var_1296 = const()[name = tensor("op_1296"), val = tensor(0x1p+9)]; + tensor var_1297 = mul(x = var_1295, y = var_1296)[name = tensor("op_1297")]; + tensor write_indices_float_11 = sub(x = ts_17, y = var_1297)[name = tensor("write_indices_float_11")]; + tensor var_1304_dtype_0 = const()[name = tensor("op_1304_dtype_0"), val = tensor("int32")]; + tensor write_indices_5_reps_0 = const()[name = tensor("write_indices_5_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1304 = cast(dtype = var_1304_dtype_0, x = write_indices_float_11)[name = tensor("cast_453")]; + tensor write_indices_5 = tile(reps = write_indices_5_reps_0, x = var_1304)[name = tensor("write_indices_5")]; + tensor var_1312_begin_0 = const()[name = tensor("op_1312_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1312_end_0 = const()[name = tensor("op_1312_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1312_end_mask_0 = const()[name = tensor("op_1312_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1312_squeeze_mask_0 = const()[name = tensor("op_1312_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1312 = slice_by_index(begin = var_1312_begin_0, end = var_1312_end_0, end_mask = var_1312_end_mask_0, squeeze_mask = var_1312_squeeze_mask_0, x = cache2)[name = tensor("op_1312")]; + tensor var_1314_axis_0 = const()[name = tensor("op_1314_axis_0"), val = tensor(1)]; + tensor var_1314_mode_0 = const()[name = tensor("op_1314_mode_0"), val = tensor("update")]; + tensor var_1314_validate_indices_0 = const()[name = tensor("op_1314_validate_indices_0"), val = tensor(false)]; + tensor var_1314 = scatter_along_axis(axis = var_1314_axis_0, data = var_1312, indices = write_indices_5, mode = var_1314_mode_0, updates = k_11, validate_indices = var_1314_validate_indices_0)[name = tensor("op_1314")]; + tensor concat_16 = const()[name = tensor("concat_16"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_52 = const()[name = tensor("shape_52"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_4 = const()[name = tensor("reduce_prod_4"), val = tensor(1048576)]; + tensor range_1d_4_start_0 = const()[name = tensor("range_1d_4_start_0"), val = tensor(0)]; + tensor range_1d_4_step_0 = const()[name = tensor("range_1d_4_step_0"), val = tensor(1)]; + tensor range_1d_4 = range_1d(end = reduce_prod_4, start = range_1d_4_start_0, step = range_1d_4_step_0)[name = tensor("range_1d_4")]; + tensor reshape_20 = reshape(shape = shape_52, x = range_1d_4)[name = tensor("reshape_20")]; + tensor slice_by_index_4 = slice_by_index(begin = concat_16, begin_mask = new_cache_5_internal_tensor_assign_1_begin_mask_0, end = concat_17, end_mask = new_cache_5_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_1_stride_0, x = reshape_20)[name = tensor("slice_by_index_4")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([-1])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = slice_by_index_4)[name = tensor("reshape_21")]; + tensor reshape_22_shape_0 = const()[name = tensor("reshape_22_shape_0"), val = tensor([-1])]; + tensor reshape_22 = reshape(shape = reshape_22_shape_0, x = var_1314)[name = tensor("reshape_22")]; + tensor reshape_23_shape_0 = const()[name = tensor("reshape_23_shape_0"), val = tensor([-1])]; + tensor reshape_23 = reshape(shape = reshape_23_shape_0, x = cache2)[name = tensor("reshape_23")]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4_validate_indices_0 = const()[name = tensor("scatter_4_validate_indices_0"), val = tensor(false)]; + tensor scatter_4 = scatter(axis = scatter_4_axis_0, data = reshape_23, indices = reshape_21, mode = scatter_4_mode_0, updates = reshape_22, validate_indices = scatter_4_validate_indices_0)[name = tensor("scatter_4")]; + tensor reshape_24 = reshape(shape = shape_52, x = scatter_4)[name = tensor("reshape_24")]; + tensor var_1322_begin_0 = const()[name = tensor("op_1322_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1322_end_0 = const()[name = tensor("op_1322_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1322_end_mask_0 = const()[name = tensor("op_1322_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1322_squeeze_mask_0 = const()[name = tensor("op_1322_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1322 = slice_by_index(begin = var_1322_begin_0, end = var_1322_end_0, end_mask = var_1322_end_mask_0, squeeze_mask = var_1322_squeeze_mask_0, x = reshape_24)[name = tensor("op_1322")]; + tensor var_1324_axis_0 = const()[name = tensor("op_1324_axis_0"), val = tensor(1)]; + tensor var_1324_mode_0 = const()[name = tensor("op_1324_mode_0"), val = tensor("update")]; + tensor var_1324_validate_indices_0 = const()[name = tensor("op_1324_validate_indices_0"), val = tensor(false)]; + tensor var_1324 = scatter_along_axis(axis = var_1324_axis_0, data = var_1322, indices = write_indices_5, mode = var_1324_mode_0, updates = v_5, validate_indices = var_1324_validate_indices_0)[name = tensor("op_1324")]; + tensor concat_18 = const()[name = tensor("concat_18"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_19 = const()[name = tensor("concat_19"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_5_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_5_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_5_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_5_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_53 = const()[name = tensor("shape_53"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_5 = const()[name = tensor("reduce_prod_5"), val = tensor(1048576)]; + tensor range_1d_5_start_0 = const()[name = tensor("range_1d_5_start_0"), val = tensor(0)]; + tensor range_1d_5_step_0 = const()[name = tensor("range_1d_5_step_0"), val = tensor(1)]; + tensor range_1d_5 = range_1d(end = reduce_prod_5, start = range_1d_5_start_0, step = range_1d_5_step_0)[name = tensor("range_1d_5")]; + tensor reshape_25 = reshape(shape = shape_53, x = range_1d_5)[name = tensor("reshape_25")]; + tensor slice_by_index_5 = slice_by_index(begin = concat_18, begin_mask = new_cache_5_internal_tensor_assign_2_begin_mask_0, end = concat_19, end_mask = new_cache_5_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_5_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_5_internal_tensor_assign_2_stride_0, x = reshape_25)[name = tensor("slice_by_index_5")]; + tensor reshape_26_shape_0 = const()[name = tensor("reshape_26_shape_0"), val = tensor([-1])]; + tensor reshape_26 = reshape(shape = reshape_26_shape_0, x = slice_by_index_5)[name = tensor("reshape_26")]; + tensor reshape_27_shape_0 = const()[name = tensor("reshape_27_shape_0"), val = tensor([-1])]; + tensor reshape_27 = reshape(shape = reshape_27_shape_0, x = var_1324)[name = tensor("reshape_27")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([-1])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = reshape_24)[name = tensor("reshape_28")]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5_validate_indices_0 = const()[name = tensor("scatter_5_validate_indices_0"), val = tensor(false)]; + tensor scatter_5 = scatter(axis = scatter_5_axis_0, data = reshape_28, indices = reshape_26, mode = scatter_5_mode_0, updates = reshape_27, validate_indices = scatter_5_validate_indices_0)[name = tensor("scatter_5")]; + tensor new_cache_5_internal_tensor_assign_2 = reshape(shape = shape_53, x = scatter_5)[name = tensor("reshape_29")]; + tensor keys_13_begin_0 = const()[name = tensor("keys_13_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_13_end_0 = const()[name = tensor("keys_13_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_13_end_mask_0 = const()[name = tensor("keys_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_13_squeeze_mask_0 = const()[name = tensor("keys_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_13 = slice_by_index(begin = keys_13_begin_0, end = keys_13_end_0, end_mask = keys_13_end_mask_0, squeeze_mask = keys_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("keys_13")]; + tensor values_13_begin_0 = const()[name = tensor("values_13_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_13_end_0 = const()[name = tensor("values_13_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_13_end_mask_0 = const()[name = tensor("values_13_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_13_squeeze_mask_0 = const()[name = tensor("values_13_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_13 = slice_by_index(begin = values_13_begin_0, end = values_13_end_0, end_mask = values_13_end_mask_0, squeeze_mask = values_13_squeeze_mask_0, x = new_cache_5_internal_tensor_assign_2)[name = tensor("values_13")]; + tensor var_1336 = not_equal(x = keys_13, y = keys_13)[name = tensor("op_1336")]; + tensor keys_15 = select(a = var_504, b = keys_13, cond = var_1336)[name = tensor("keys_15")]; + tensor var_1344 = not_equal(x = values_13, y = values_13)[name = tensor("op_1344")]; + tensor values_15 = select(a = var_504, b = values_13, cond = var_1344)[name = tensor("values_15")]; + tensor var_1368 = const()[name = tensor("op_1368"), val = tensor([0, 2, 1, 3])]; + tensor var_1381 = const()[name = tensor("op_1381"), val = tensor([1, 1, 1])]; + tensor var_1382 = reshape(shape = var_1381, x = position2)[name = tensor("op_1382")]; + tensor var_1399 = const()[name = tensor("op_1399"), val = tensor(0x1p+0)]; + tensor valid_len_5 = add(x = var_1382, y = var_1399)[name = tensor("valid_len_5")]; + tensor valid_mask_5 = less(x = k_positions_1_promoted, y = valid_len_5)[name = tensor("valid_mask_5")]; + tensor causal_mask_5 = less_equal(x = k_positions_1_promoted, y = var_1382)[name = tensor("causal_mask_5")]; + tensor attn_mask_9 = logical_and(x = valid_mask_5, y = causal_mask_5)[name = tensor("attn_mask_9")]; + tensor attn_mask_11_axes_0 = const()[name = tensor("attn_mask_11_axes_0"), val = tensor([1])]; + tensor attn_mask_11 = expand_dims(axes = attn_mask_11_axes_0, x = attn_mask_9)[name = tensor("attn_mask_11")]; + tensor var_1411 = const()[name = tensor("op_1411"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1417_transpose_x_0 = const()[name = tensor("op_1417_transpose_x_0"), val = tensor(false)]; + tensor var_1417_transpose_y_0 = const()[name = tensor("op_1417_transpose_y_0"), val = tensor(false)]; + tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = keys_15)[name = tensor("transpose_205")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = q_15)[name = tensor("transpose_206")]; + tensor var_1417 = matmul(transpose_x = var_1417_transpose_x_0, transpose_y = var_1417_transpose_y_0, x = transpose_76, y = transpose_77)[name = tensor("op_1417")]; + tensor attn_weights_13 = mul(x = var_1417, y = var_1411)[name = tensor("attn_weights_13")]; + tensor var_1419 = logical_not(x = attn_mask_11)[name = tensor("op_1419")]; + tensor var_1420 = const()[name = tensor("op_1420"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_15 = select(a = var_1420, b = attn_weights_13, cond = var_1419)[name = tensor("attn_weights_15")]; + tensor var_1422 = const()[name = tensor("op_1422"), val = tensor(-1)]; + tensor attn_weights_17 = softmax(axis = var_1422, x = attn_weights_15)[name = tensor("attn_weights_17")]; + tensor attn_output_5_transpose_x_0 = const()[name = tensor("attn_output_5_transpose_x_0"), val = tensor(false)]; + tensor attn_output_5_transpose_y_0 = const()[name = tensor("attn_output_5_transpose_y_0"), val = tensor(false)]; + tensor values_17 = transpose(perm = var_1368, x = values_15)[name = tensor("transpose_207")]; + tensor attn_output_5 = matmul(transpose_x = attn_output_5_transpose_x_0, transpose_y = attn_output_5_transpose_y_0, x = attn_weights_17, y = values_17)[name = tensor("attn_output_5")]; + tensor var_1430 = const()[name = tensor("op_1430"), val = tensor([0, 2, 1, 3])]; + tensor var_1433 = const()[name = tensor("op_1433"), val = tensor([1, 1, 1024])]; + tensor var_1431 = transpose(perm = var_1430, x = attn_output_5)[name = tensor("transpose_204")]; + tensor input_25 = reshape(shape = var_1433, x = var_1431)[name = tensor("input_25")]; + tensor attn_out_5 = linear(bias = linear_0_bias_0, weight = attn2_out_proj_weight, x = input_25)[name = tensor("linear_10")]; + tensor var_1439 = const()[name = tensor("op_1439"), val = tensor(0x1p+0)]; + tensor var_1440 = add(x = position2, y = var_1439)[name = tensor("op_1440")]; + tensor input_27 = add(x = input_23, y = attn_out_5)[name = tensor("input_27")]; + tensor var_1444 = const()[name = tensor("op_1444"), val = tensor(0x1.4f8b58p-17)]; + tensor input_29_axes_0 = const()[name = tensor("input_29_axes_0"), val = tensor([-1])]; + tensor input_29 = layer_norm(axes = input_29_axes_0, beta = norm2_2_bias, epsilon = var_1444, gamma = norm2_2_weight, x = input_27)[name = tensor("input_29")]; + tensor var_1452 = linear(bias = linear_3_bias_0, weight = linear2_1_weight, x = input_29)[name = tensor("linear_11")]; + tensor input_31_mode_0 = const()[name = tensor("input_31_mode_0"), val = tensor("EXACT")]; + tensor input_31 = gelu(mode = input_31_mode_0, x = var_1452)[name = tensor("input_31")]; + tensor ffn_out_5 = linear(bias = linear_0_bias_0, weight = linear2_2_weight, x = input_31)[name = tensor("linear_12")]; + tensor input_33 = add(x = input_27, y = ffn_out_5)[name = tensor("input_33")]; + tensor var_1461 = const()[name = tensor("op_1461"), val = tensor(0x1.4f8b58p-17)]; + tensor x_7_axes_0 = const()[name = tensor("x_7_axes_0"), val = tensor([-1])]; + tensor x_7 = layer_norm(axes = x_7_axes_0, beta = norm3_1_bias, epsilon = var_1461, gamma = norm3_1_weight, x = input_33)[name = tensor("x_7")]; + tensor var_1493 = linear(bias = linear_1_bias_0, weight = attn3_in_proj_weight, x = x_7)[name = tensor("linear_13")]; + tensor var_1497 = const()[name = tensor("op_1497"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_7 = reshape(shape = var_1497, x = var_1493)[name = tensor("qkv_7")]; + tensor q_19_begin_0 = const()[name = tensor("q_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_19_end_0 = const()[name = tensor("q_19_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_19_end_mask_0 = const()[name = tensor("q_19_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_19_squeeze_mask_0 = const()[name = tensor("q_19_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_19 = slice_by_index(begin = q_19_begin_0, end = q_19_end_0, end_mask = q_19_end_mask_0, squeeze_mask = q_19_squeeze_mask_0, x = qkv_7)[name = tensor("q_19")]; + tensor k_13_begin_0 = const()[name = tensor("k_13_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_13_end_0 = const()[name = tensor("k_13_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_13_end_mask_0 = const()[name = tensor("k_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_13_squeeze_mask_0 = const()[name = tensor("k_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_13 = slice_by_index(begin = k_13_begin_0, end = k_13_end_0, end_mask = k_13_end_mask_0, squeeze_mask = k_13_squeeze_mask_0, x = qkv_7)[name = tensor("k_13")]; + tensor v_7_begin_0 = const()[name = tensor("v_7_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_7_end_0 = const()[name = tensor("v_7_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_7_end_mask_0 = const()[name = tensor("v_7_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_7_squeeze_mask_0 = const()[name = tensor("v_7_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_7 = slice_by_index(begin = v_7_begin_0, end = v_7_end_0, end_mask = v_7_end_mask_0, squeeze_mask = v_7_squeeze_mask_0, x = qkv_7)[name = tensor("v_7")]; + tensor freqs_7 = const()[name = tensor("freqs_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210641600)))]; + tensor var_1601 = const()[name = tensor("op_1601"), val = tensor([1, 1, 1, 1])]; + tensor ts_23 = reshape(shape = var_1601, x = position3)[name = tensor("ts_23")]; + tensor var_1605 = const()[name = tensor("op_1605"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_7 = reshape(shape = var_1605, x = q_19)[name = tensor("q_complex_7")]; + tensor var_1609 = const()[name = tensor("op_1609"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_7 = reshape(shape = var_1609, x = k_13)[name = tensor("k_complex_7")]; + tensor var_1613_begin_0 = const()[name = tensor("op_1613_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1613_end_0 = const()[name = tensor("op_1613_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1613_end_mask_0 = const()[name = tensor("op_1613_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1613_squeeze_mask_0 = const()[name = tensor("op_1613_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1613 = slice_by_index(begin = var_1613_begin_0, end = var_1613_end_0, end_mask = var_1613_end_mask_0, squeeze_mask = var_1613_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1613")]; + tensor var_1621_begin_0 = const()[name = tensor("op_1621_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1621_end_0 = const()[name = tensor("op_1621_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1621_end_mask_0 = const()[name = tensor("op_1621_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1621_squeeze_mask_0 = const()[name = tensor("op_1621_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1621 = slice_by_index(begin = var_1621_begin_0, end = var_1621_end_0, end_mask = var_1621_end_mask_0, squeeze_mask = var_1621_squeeze_mask_0, x = q_complex_7)[name = tensor("op_1621")]; + tensor var_1629_begin_0 = const()[name = tensor("op_1629_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1629_end_0 = const()[name = tensor("op_1629_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_1629_end_mask_0 = const()[name = tensor("op_1629_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1629_squeeze_mask_0 = const()[name = tensor("op_1629_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1629 = slice_by_index(begin = var_1629_begin_0, end = var_1629_end_0, end_mask = var_1629_end_mask_0, squeeze_mask = var_1629_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1629")]; + tensor var_1637_begin_0 = const()[name = tensor("op_1637_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_1637_end_0 = const()[name = tensor("op_1637_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_1637_end_mask_0 = const()[name = tensor("op_1637_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_1637_squeeze_mask_0 = const()[name = tensor("op_1637_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_1637 = slice_by_index(begin = var_1637_begin_0, end = var_1637_end_0, end_mask = var_1637_end_mask_0, squeeze_mask = var_1637_squeeze_mask_0, x = k_complex_7)[name = tensor("op_1637")]; + tensor var_1643 = mul(x = freqs_7, y = ts_23)[name = tensor("op_1643")]; + tensor rotr_7 = cos(x = var_1643)[name = tensor("rotr_7")]; + tensor roti_7 = sin(x = var_1643)[name = tensor("roti_7")]; + tensor var_1647 = mul(x = var_1613, y = rotr_7)[name = tensor("op_1647")]; + tensor var_1648 = mul(x = var_1621, y = roti_7)[name = tensor("op_1648")]; + tensor qor_13 = sub(x = var_1647, y = var_1648)[name = tensor("qor_13")]; + tensor var_1651 = mul(x = var_1613, y = roti_7)[name = tensor("op_1651")]; + tensor var_1652 = mul(x = var_1621, y = rotr_7)[name = tensor("op_1652")]; + tensor qoi_13 = add(x = var_1651, y = var_1652)[name = tensor("qoi_13")]; + tensor var_1655 = mul(x = var_1629, y = rotr_7)[name = tensor("op_1655")]; + tensor var_1656 = mul(x = var_1637, y = roti_7)[name = tensor("op_1656")]; + tensor kor_13 = sub(x = var_1655, y = var_1656)[name = tensor("kor_13")]; + tensor var_1659 = mul(x = var_1629, y = roti_7)[name = tensor("op_1659")]; + tensor var_1660 = mul(x = var_1637, y = rotr_7)[name = tensor("op_1660")]; + tensor koi_13 = add(x = var_1659, y = var_1660)[name = tensor("koi_13")]; + tensor qo_7_axis_0 = const()[name = tensor("qo_7_axis_0"), val = tensor(-1)]; + tensor qo_7 = stack(axis = qo_7_axis_0, values = (qor_13, qoi_13))[name = tensor("qo_7")]; + tensor ko_7_axis_0 = const()[name = tensor("ko_7_axis_0"), val = tensor(-1)]; + tensor ko_7 = stack(axis = ko_7_axis_0, values = (kor_13, koi_13))[name = tensor("ko_7")]; + tensor var_1689 = const()[name = tensor("op_1689"), val = tensor([1, 1, 16, 64])]; + tensor q_21 = reshape(shape = var_1689, x = qo_7)[name = tensor("q_21")]; + tensor var_1691 = const()[name = tensor("op_1691"), val = tensor([1, 1, 16, 64])]; + tensor k_15 = reshape(shape = var_1691, x = ko_7)[name = tensor("k_15")]; + tensor _inversed_1713_y_0 = const()[name = tensor("_inversed_1713_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_1713 = mul(x = ts_23, y = _inversed_1713_y_0)[name = tensor("_inversed_1713")]; + tensor var_1714 = floor(x = _inversed_1713)[name = tensor("op_1714")]; + tensor var_1715 = const()[name = tensor("op_1715"), val = tensor(0x1p+9)]; + tensor var_1716 = mul(x = var_1714, y = var_1715)[name = tensor("op_1716")]; + tensor write_indices_float_15 = sub(x = ts_23, y = var_1716)[name = tensor("write_indices_float_15")]; + tensor var_1723_dtype_0 = const()[name = tensor("op_1723_dtype_0"), val = tensor("int32")]; + tensor write_indices_7_reps_0 = const()[name = tensor("write_indices_7_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_1723 = cast(dtype = var_1723_dtype_0, x = write_indices_float_15)[name = tensor("cast_452")]; + tensor write_indices_7 = tile(reps = write_indices_7_reps_0, x = var_1723)[name = tensor("write_indices_7")]; + tensor var_1731_begin_0 = const()[name = tensor("op_1731_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_1731_end_0 = const()[name = tensor("op_1731_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_1731_end_mask_0 = const()[name = tensor("op_1731_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1731_squeeze_mask_0 = const()[name = tensor("op_1731_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1731 = slice_by_index(begin = var_1731_begin_0, end = var_1731_end_0, end_mask = var_1731_end_mask_0, squeeze_mask = var_1731_squeeze_mask_0, x = cache3)[name = tensor("op_1731")]; + tensor var_1733_axis_0 = const()[name = tensor("op_1733_axis_0"), val = tensor(1)]; + tensor var_1733_mode_0 = const()[name = tensor("op_1733_mode_0"), val = tensor("update")]; + tensor var_1733_validate_indices_0 = const()[name = tensor("op_1733_validate_indices_0"), val = tensor(false)]; + tensor var_1733 = scatter_along_axis(axis = var_1733_axis_0, data = var_1731, indices = write_indices_7, mode = var_1733_mode_0, updates = k_15, validate_indices = var_1733_validate_indices_0)[name = tensor("op_1733")]; + tensor concat_23 = const()[name = tensor("concat_23"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_24 = const()[name = tensor("concat_24"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_54 = const()[name = tensor("shape_54"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_6 = const()[name = tensor("reduce_prod_6"), val = tensor(1048576)]; + tensor range_1d_6_start_0 = const()[name = tensor("range_1d_6_start_0"), val = tensor(0)]; + tensor range_1d_6_step_0 = const()[name = tensor("range_1d_6_step_0"), val = tensor(1)]; + tensor range_1d_6 = range_1d(end = reduce_prod_6, start = range_1d_6_start_0, step = range_1d_6_step_0)[name = tensor("range_1d_6")]; + tensor reshape_30 = reshape(shape = shape_54, x = range_1d_6)[name = tensor("reshape_30")]; + tensor slice_by_index_6 = slice_by_index(begin = concat_23, begin_mask = new_cache_7_internal_tensor_assign_1_begin_mask_0, end = concat_24, end_mask = new_cache_7_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_1_stride_0, x = reshape_30)[name = tensor("slice_by_index_6")]; + tensor reshape_31_shape_0 = const()[name = tensor("reshape_31_shape_0"), val = tensor([-1])]; + tensor reshape_31 = reshape(shape = reshape_31_shape_0, x = slice_by_index_6)[name = tensor("reshape_31")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([-1])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = var_1733)[name = tensor("reshape_32")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([-1])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = cache3)[name = tensor("reshape_33")]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6_validate_indices_0 = const()[name = tensor("scatter_6_validate_indices_0"), val = tensor(false)]; + tensor scatter_6 = scatter(axis = scatter_6_axis_0, data = reshape_33, indices = reshape_31, mode = scatter_6_mode_0, updates = reshape_32, validate_indices = scatter_6_validate_indices_0)[name = tensor("scatter_6")]; + tensor reshape_34 = reshape(shape = shape_54, x = scatter_6)[name = tensor("reshape_34")]; + tensor var_1741_begin_0 = const()[name = tensor("op_1741_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_1741_end_0 = const()[name = tensor("op_1741_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_1741_end_mask_0 = const()[name = tensor("op_1741_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_1741_squeeze_mask_0 = const()[name = tensor("op_1741_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_1741 = slice_by_index(begin = var_1741_begin_0, end = var_1741_end_0, end_mask = var_1741_end_mask_0, squeeze_mask = var_1741_squeeze_mask_0, x = reshape_34)[name = tensor("op_1741")]; + tensor var_1743_axis_0 = const()[name = tensor("op_1743_axis_0"), val = tensor(1)]; + tensor var_1743_mode_0 = const()[name = tensor("op_1743_mode_0"), val = tensor("update")]; + tensor var_1743_validate_indices_0 = const()[name = tensor("op_1743_validate_indices_0"), val = tensor(false)]; + tensor var_1743 = scatter_along_axis(axis = var_1743_axis_0, data = var_1741, indices = write_indices_7, mode = var_1743_mode_0, updates = v_7, validate_indices = var_1743_validate_indices_0)[name = tensor("op_1743")]; + tensor concat_25 = const()[name = tensor("concat_25"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_26 = const()[name = tensor("concat_26"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_7_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_7_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_7_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_7_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_55 = const()[name = tensor("shape_55"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_7 = const()[name = tensor("reduce_prod_7"), val = tensor(1048576)]; + tensor range_1d_7_start_0 = const()[name = tensor("range_1d_7_start_0"), val = tensor(0)]; + tensor range_1d_7_step_0 = const()[name = tensor("range_1d_7_step_0"), val = tensor(1)]; + tensor range_1d_7 = range_1d(end = reduce_prod_7, start = range_1d_7_start_0, step = range_1d_7_step_0)[name = tensor("range_1d_7")]; + tensor reshape_35 = reshape(shape = shape_55, x = range_1d_7)[name = tensor("reshape_35")]; + tensor slice_by_index_7 = slice_by_index(begin = concat_25, begin_mask = new_cache_7_internal_tensor_assign_2_begin_mask_0, end = concat_26, end_mask = new_cache_7_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_7_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_7_internal_tensor_assign_2_stride_0, x = reshape_35)[name = tensor("slice_by_index_7")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([-1])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = slice_by_index_7)[name = tensor("reshape_36")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([-1])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = var_1743)[name = tensor("reshape_37")]; + tensor reshape_38_shape_0 = const()[name = tensor("reshape_38_shape_0"), val = tensor([-1])]; + tensor reshape_38 = reshape(shape = reshape_38_shape_0, x = reshape_34)[name = tensor("reshape_38")]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7_validate_indices_0 = const()[name = tensor("scatter_7_validate_indices_0"), val = tensor(false)]; + tensor scatter_7 = scatter(axis = scatter_7_axis_0, data = reshape_38, indices = reshape_36, mode = scatter_7_mode_0, updates = reshape_37, validate_indices = scatter_7_validate_indices_0)[name = tensor("scatter_7")]; + tensor new_cache_7_internal_tensor_assign_2 = reshape(shape = shape_55, x = scatter_7)[name = tensor("reshape_39")]; + tensor keys_19_begin_0 = const()[name = tensor("keys_19_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_19_end_0 = const()[name = tensor("keys_19_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_19_end_mask_0 = const()[name = tensor("keys_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_19_squeeze_mask_0 = const()[name = tensor("keys_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_19 = slice_by_index(begin = keys_19_begin_0, end = keys_19_end_0, end_mask = keys_19_end_mask_0, squeeze_mask = keys_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("keys_19")]; + tensor values_19_begin_0 = const()[name = tensor("values_19_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_19_end_0 = const()[name = tensor("values_19_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_19_end_mask_0 = const()[name = tensor("values_19_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_19_squeeze_mask_0 = const()[name = tensor("values_19_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_19 = slice_by_index(begin = values_19_begin_0, end = values_19_end_0, end_mask = values_19_end_mask_0, squeeze_mask = values_19_squeeze_mask_0, x = new_cache_7_internal_tensor_assign_2)[name = tensor("values_19")]; + tensor var_1755 = not_equal(x = keys_19, y = keys_19)[name = tensor("op_1755")]; + tensor keys_21 = select(a = var_504, b = keys_19, cond = var_1755)[name = tensor("keys_21")]; + tensor var_1763 = not_equal(x = values_19, y = values_19)[name = tensor("op_1763")]; + tensor values_21 = select(a = var_504, b = values_19, cond = var_1763)[name = tensor("values_21")]; + tensor var_1787 = const()[name = tensor("op_1787"), val = tensor([0, 2, 1, 3])]; + tensor var_1800 = const()[name = tensor("op_1800"), val = tensor([1, 1, 1])]; + tensor var_1801 = reshape(shape = var_1800, x = position3)[name = tensor("op_1801")]; + tensor var_1818 = const()[name = tensor("op_1818"), val = tensor(0x1p+0)]; + tensor valid_len_7 = add(x = var_1801, y = var_1818)[name = tensor("valid_len_7")]; + tensor valid_mask_7 = less(x = k_positions_1_promoted, y = valid_len_7)[name = tensor("valid_mask_7")]; + tensor causal_mask_7 = less_equal(x = k_positions_1_promoted, y = var_1801)[name = tensor("causal_mask_7")]; + tensor attn_mask_13 = logical_and(x = valid_mask_7, y = causal_mask_7)[name = tensor("attn_mask_13")]; + tensor attn_mask_15_axes_0 = const()[name = tensor("attn_mask_15_axes_0"), val = tensor([1])]; + tensor attn_mask_15 = expand_dims(axes = attn_mask_15_axes_0, x = attn_mask_13)[name = tensor("attn_mask_15")]; + tensor var_1830 = const()[name = tensor("op_1830"), val = tensor([0x1.fffe5cp-4])]; + tensor var_1836_transpose_x_0 = const()[name = tensor("op_1836_transpose_x_0"), val = tensor(false)]; + tensor var_1836_transpose_y_0 = const()[name = tensor("op_1836_transpose_y_0"), val = tensor(false)]; + tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = keys_21)[name = tensor("transpose_201")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = q_21)[name = tensor("transpose_202")]; + tensor var_1836 = matmul(transpose_x = var_1836_transpose_x_0, transpose_y = var_1836_transpose_y_0, x = transpose_78, y = transpose_79)[name = tensor("op_1836")]; + tensor attn_weights_19 = mul(x = var_1836, y = var_1830)[name = tensor("attn_weights_19")]; + tensor var_1838 = logical_not(x = attn_mask_15)[name = tensor("op_1838")]; + tensor var_1839 = const()[name = tensor("op_1839"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_21 = select(a = var_1839, b = attn_weights_19, cond = var_1838)[name = tensor("attn_weights_21")]; + tensor var_1841 = const()[name = tensor("op_1841"), val = tensor(-1)]; + tensor attn_weights_23 = softmax(axis = var_1841, x = attn_weights_21)[name = tensor("attn_weights_23")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor values_23 = transpose(perm = var_1787, x = values_21)[name = tensor("transpose_203")]; + tensor attn_output_7 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = attn_weights_23, y = values_23)[name = tensor("attn_output_7")]; + tensor var_1849 = const()[name = tensor("op_1849"), val = tensor([0, 2, 1, 3])]; + tensor var_1852 = const()[name = tensor("op_1852"), val = tensor([1, 1, 1024])]; + tensor var_1850 = transpose(perm = var_1849, x = attn_output_7)[name = tensor("transpose_200")]; + tensor input_35 = reshape(shape = var_1852, x = var_1850)[name = tensor("input_35")]; + tensor attn_out_7 = linear(bias = linear_0_bias_0, weight = attn3_out_proj_weight, x = input_35)[name = tensor("linear_14")]; + tensor var_1858 = const()[name = tensor("op_1858"), val = tensor(0x1p+0)]; + tensor var_1859 = add(x = position3, y = var_1858)[name = tensor("op_1859")]; + tensor input_37 = add(x = input_33, y = attn_out_7)[name = tensor("input_37")]; + tensor var_1863 = const()[name = tensor("op_1863"), val = tensor(0x1.4f8b58p-17)]; + tensor input_39_axes_0 = const()[name = tensor("input_39_axes_0"), val = tensor([-1])]; + tensor input_39 = layer_norm(axes = input_39_axes_0, beta = norm3_2_bias, epsilon = var_1863, gamma = norm3_2_weight, x = input_37)[name = tensor("input_39")]; + tensor var_1871 = linear(bias = linear_3_bias_0, weight = linear3_1_weight, x = input_39)[name = tensor("linear_15")]; + tensor input_41_mode_0 = const()[name = tensor("input_41_mode_0"), val = tensor("EXACT")]; + tensor input_41 = gelu(mode = input_41_mode_0, x = var_1871)[name = tensor("input_41")]; + tensor ffn_out_7 = linear(bias = linear_0_bias_0, weight = linear3_2_weight, x = input_41)[name = tensor("linear_16")]; + tensor input_43 = add(x = input_37, y = ffn_out_7)[name = tensor("input_43")]; + tensor var_1880 = const()[name = tensor("op_1880"), val = tensor(0x1.4f8b58p-17)]; + tensor x_9_axes_0 = const()[name = tensor("x_9_axes_0"), val = tensor([-1])]; + tensor x_9 = layer_norm(axes = x_9_axes_0, beta = norm4_1_bias, epsilon = var_1880, gamma = norm4_1_weight, x = input_43)[name = tensor("x_9")]; + tensor var_1912 = linear(bias = linear_1_bias_0, weight = attn4_in_proj_weight, x = x_9)[name = tensor("linear_17")]; + tensor var_1916 = const()[name = tensor("op_1916"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_9 = reshape(shape = var_1916, x = var_1912)[name = tensor("qkv_9")]; + tensor q_25_begin_0 = const()[name = tensor("q_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_25_end_0 = const()[name = tensor("q_25_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_25_end_mask_0 = const()[name = tensor("q_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_25_squeeze_mask_0 = const()[name = tensor("q_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_25 = slice_by_index(begin = q_25_begin_0, end = q_25_end_0, end_mask = q_25_end_mask_0, squeeze_mask = q_25_squeeze_mask_0, x = qkv_9)[name = tensor("q_25")]; + tensor k_17_begin_0 = const()[name = tensor("k_17_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_17_end_0 = const()[name = tensor("k_17_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_17_end_mask_0 = const()[name = tensor("k_17_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_17_squeeze_mask_0 = const()[name = tensor("k_17_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_17 = slice_by_index(begin = k_17_begin_0, end = k_17_end_0, end_mask = k_17_end_mask_0, squeeze_mask = k_17_squeeze_mask_0, x = qkv_9)[name = tensor("k_17")]; + tensor v_9_begin_0 = const()[name = tensor("v_9_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_9_end_0 = const()[name = tensor("v_9_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_9_end_mask_0 = const()[name = tensor("v_9_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_9_squeeze_mask_0 = const()[name = tensor("v_9_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_9 = slice_by_index(begin = v_9_begin_0, end = v_9_end_0, end_mask = v_9_end_mask_0, squeeze_mask = v_9_squeeze_mask_0, x = qkv_9)[name = tensor("v_9")]; + tensor freqs_9 = const()[name = tensor("freqs_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210641792)))]; + tensor var_2020 = const()[name = tensor("op_2020"), val = tensor([1, 1, 1, 1])]; + tensor ts_29 = reshape(shape = var_2020, x = position4)[name = tensor("ts_29")]; + tensor var_2024 = const()[name = tensor("op_2024"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_9 = reshape(shape = var_2024, x = q_25)[name = tensor("q_complex_9")]; + tensor var_2028 = const()[name = tensor("op_2028"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_9 = reshape(shape = var_2028, x = k_17)[name = tensor("k_complex_9")]; + tensor var_2032_begin_0 = const()[name = tensor("op_2032_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2032_end_0 = const()[name = tensor("op_2032_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2032_end_mask_0 = const()[name = tensor("op_2032_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2032_squeeze_mask_0 = const()[name = tensor("op_2032_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2032 = slice_by_index(begin = var_2032_begin_0, end = var_2032_end_0, end_mask = var_2032_end_mask_0, squeeze_mask = var_2032_squeeze_mask_0, x = q_complex_9)[name = tensor("op_2032")]; + tensor var_2040_begin_0 = const()[name = tensor("op_2040_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2040_end_0 = const()[name = tensor("op_2040_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2040_end_mask_0 = const()[name = tensor("op_2040_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2040_squeeze_mask_0 = const()[name = tensor("op_2040_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2040 = slice_by_index(begin = var_2040_begin_0, end = var_2040_end_0, end_mask = var_2040_end_mask_0, squeeze_mask = var_2040_squeeze_mask_0, x = q_complex_9)[name = tensor("op_2040")]; + tensor var_2048_begin_0 = const()[name = tensor("op_2048_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2048_end_0 = const()[name = tensor("op_2048_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2048_end_mask_0 = const()[name = tensor("op_2048_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2048_squeeze_mask_0 = const()[name = tensor("op_2048_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2048 = slice_by_index(begin = var_2048_begin_0, end = var_2048_end_0, end_mask = var_2048_end_mask_0, squeeze_mask = var_2048_squeeze_mask_0, x = k_complex_9)[name = tensor("op_2048")]; + tensor var_2056_begin_0 = const()[name = tensor("op_2056_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2056_end_0 = const()[name = tensor("op_2056_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2056_end_mask_0 = const()[name = tensor("op_2056_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2056_squeeze_mask_0 = const()[name = tensor("op_2056_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2056 = slice_by_index(begin = var_2056_begin_0, end = var_2056_end_0, end_mask = var_2056_end_mask_0, squeeze_mask = var_2056_squeeze_mask_0, x = k_complex_9)[name = tensor("op_2056")]; + tensor var_2062 = mul(x = freqs_9, y = ts_29)[name = tensor("op_2062")]; + tensor rotr_9 = cos(x = var_2062)[name = tensor("rotr_9")]; + tensor roti_9 = sin(x = var_2062)[name = tensor("roti_9")]; + tensor var_2066 = mul(x = var_2032, y = rotr_9)[name = tensor("op_2066")]; + tensor var_2067 = mul(x = var_2040, y = roti_9)[name = tensor("op_2067")]; + tensor qor_17 = sub(x = var_2066, y = var_2067)[name = tensor("qor_17")]; + tensor var_2070 = mul(x = var_2032, y = roti_9)[name = tensor("op_2070")]; + tensor var_2071 = mul(x = var_2040, y = rotr_9)[name = tensor("op_2071")]; + tensor qoi_17 = add(x = var_2070, y = var_2071)[name = tensor("qoi_17")]; + tensor var_2074 = mul(x = var_2048, y = rotr_9)[name = tensor("op_2074")]; + tensor var_2075 = mul(x = var_2056, y = roti_9)[name = tensor("op_2075")]; + tensor kor_17 = sub(x = var_2074, y = var_2075)[name = tensor("kor_17")]; + tensor var_2078 = mul(x = var_2048, y = roti_9)[name = tensor("op_2078")]; + tensor var_2079 = mul(x = var_2056, y = rotr_9)[name = tensor("op_2079")]; + tensor koi_17 = add(x = var_2078, y = var_2079)[name = tensor("koi_17")]; + tensor qo_9_axis_0 = const()[name = tensor("qo_9_axis_0"), val = tensor(-1)]; + tensor qo_9 = stack(axis = qo_9_axis_0, values = (qor_17, qoi_17))[name = tensor("qo_9")]; + tensor ko_9_axis_0 = const()[name = tensor("ko_9_axis_0"), val = tensor(-1)]; + tensor ko_9 = stack(axis = ko_9_axis_0, values = (kor_17, koi_17))[name = tensor("ko_9")]; + tensor var_2108 = const()[name = tensor("op_2108"), val = tensor([1, 1, 16, 64])]; + tensor q_27 = reshape(shape = var_2108, x = qo_9)[name = tensor("q_27")]; + tensor var_2110 = const()[name = tensor("op_2110"), val = tensor([1, 1, 16, 64])]; + tensor k_19 = reshape(shape = var_2110, x = ko_9)[name = tensor("k_19")]; + tensor _inversed_2132_y_0 = const()[name = tensor("_inversed_2132_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2132 = mul(x = ts_29, y = _inversed_2132_y_0)[name = tensor("_inversed_2132")]; + tensor var_2133 = floor(x = _inversed_2132)[name = tensor("op_2133")]; + tensor var_2134 = const()[name = tensor("op_2134"), val = tensor(0x1p+9)]; + tensor var_2135 = mul(x = var_2133, y = var_2134)[name = tensor("op_2135")]; + tensor write_indices_float_19 = sub(x = ts_29, y = var_2135)[name = tensor("write_indices_float_19")]; + tensor var_2142_dtype_0 = const()[name = tensor("op_2142_dtype_0"), val = tensor("int32")]; + tensor write_indices_9_reps_0 = const()[name = tensor("write_indices_9_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2142 = cast(dtype = var_2142_dtype_0, x = write_indices_float_19)[name = tensor("cast_451")]; + tensor write_indices_9 = tile(reps = write_indices_9_reps_0, x = var_2142)[name = tensor("write_indices_9")]; + tensor var_2150_begin_0 = const()[name = tensor("op_2150_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2150_end_0 = const()[name = tensor("op_2150_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2150_end_mask_0 = const()[name = tensor("op_2150_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2150_squeeze_mask_0 = const()[name = tensor("op_2150_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2150 = slice_by_index(begin = var_2150_begin_0, end = var_2150_end_0, end_mask = var_2150_end_mask_0, squeeze_mask = var_2150_squeeze_mask_0, x = cache4)[name = tensor("op_2150")]; + tensor var_2152_axis_0 = const()[name = tensor("op_2152_axis_0"), val = tensor(1)]; + tensor var_2152_mode_0 = const()[name = tensor("op_2152_mode_0"), val = tensor("update")]; + tensor var_2152_validate_indices_0 = const()[name = tensor("op_2152_validate_indices_0"), val = tensor(false)]; + tensor var_2152 = scatter_along_axis(axis = var_2152_axis_0, data = var_2150, indices = write_indices_9, mode = var_2152_mode_0, updates = k_19, validate_indices = var_2152_validate_indices_0)[name = tensor("op_2152")]; + tensor concat_30 = const()[name = tensor("concat_30"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_31 = const()[name = tensor("concat_31"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_56 = const()[name = tensor("shape_56"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_8 = const()[name = tensor("reduce_prod_8"), val = tensor(1048576)]; + tensor range_1d_8_start_0 = const()[name = tensor("range_1d_8_start_0"), val = tensor(0)]; + tensor range_1d_8_step_0 = const()[name = tensor("range_1d_8_step_0"), val = tensor(1)]; + tensor range_1d_8 = range_1d(end = reduce_prod_8, start = range_1d_8_start_0, step = range_1d_8_step_0)[name = tensor("range_1d_8")]; + tensor reshape_40 = reshape(shape = shape_56, x = range_1d_8)[name = tensor("reshape_40")]; + tensor slice_by_index_8 = slice_by_index(begin = concat_30, begin_mask = new_cache_9_internal_tensor_assign_1_begin_mask_0, end = concat_31, end_mask = new_cache_9_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_1_stride_0, x = reshape_40)[name = tensor("slice_by_index_8")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([-1])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = slice_by_index_8)[name = tensor("reshape_41")]; + tensor reshape_42_shape_0 = const()[name = tensor("reshape_42_shape_0"), val = tensor([-1])]; + tensor reshape_42 = reshape(shape = reshape_42_shape_0, x = var_2152)[name = tensor("reshape_42")]; + tensor reshape_43_shape_0 = const()[name = tensor("reshape_43_shape_0"), val = tensor([-1])]; + tensor reshape_43 = reshape(shape = reshape_43_shape_0, x = cache4)[name = tensor("reshape_43")]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8_validate_indices_0 = const()[name = tensor("scatter_8_validate_indices_0"), val = tensor(false)]; + tensor scatter_8 = scatter(axis = scatter_8_axis_0, data = reshape_43, indices = reshape_41, mode = scatter_8_mode_0, updates = reshape_42, validate_indices = scatter_8_validate_indices_0)[name = tensor("scatter_8")]; + tensor reshape_44 = reshape(shape = shape_56, x = scatter_8)[name = tensor("reshape_44")]; + tensor var_2160_begin_0 = const()[name = tensor("op_2160_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2160_end_0 = const()[name = tensor("op_2160_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2160_end_mask_0 = const()[name = tensor("op_2160_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2160_squeeze_mask_0 = const()[name = tensor("op_2160_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2160 = slice_by_index(begin = var_2160_begin_0, end = var_2160_end_0, end_mask = var_2160_end_mask_0, squeeze_mask = var_2160_squeeze_mask_0, x = reshape_44)[name = tensor("op_2160")]; + tensor var_2162_axis_0 = const()[name = tensor("op_2162_axis_0"), val = tensor(1)]; + tensor var_2162_mode_0 = const()[name = tensor("op_2162_mode_0"), val = tensor("update")]; + tensor var_2162_validate_indices_0 = const()[name = tensor("op_2162_validate_indices_0"), val = tensor(false)]; + tensor var_2162 = scatter_along_axis(axis = var_2162_axis_0, data = var_2160, indices = write_indices_9, mode = var_2162_mode_0, updates = v_9, validate_indices = var_2162_validate_indices_0)[name = tensor("op_2162")]; + tensor concat_32 = const()[name = tensor("concat_32"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_33 = const()[name = tensor("concat_33"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_9_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_9_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_9_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_9_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_57 = const()[name = tensor("shape_57"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_9 = const()[name = tensor("reduce_prod_9"), val = tensor(1048576)]; + tensor range_1d_9_start_0 = const()[name = tensor("range_1d_9_start_0"), val = tensor(0)]; + tensor range_1d_9_step_0 = const()[name = tensor("range_1d_9_step_0"), val = tensor(1)]; + tensor range_1d_9 = range_1d(end = reduce_prod_9, start = range_1d_9_start_0, step = range_1d_9_step_0)[name = tensor("range_1d_9")]; + tensor reshape_45 = reshape(shape = shape_57, x = range_1d_9)[name = tensor("reshape_45")]; + tensor slice_by_index_9 = slice_by_index(begin = concat_32, begin_mask = new_cache_9_internal_tensor_assign_2_begin_mask_0, end = concat_33, end_mask = new_cache_9_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_9_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_9_internal_tensor_assign_2_stride_0, x = reshape_45)[name = tensor("slice_by_index_9")]; + tensor reshape_46_shape_0 = const()[name = tensor("reshape_46_shape_0"), val = tensor([-1])]; + tensor reshape_46 = reshape(shape = reshape_46_shape_0, x = slice_by_index_9)[name = tensor("reshape_46")]; + tensor reshape_47_shape_0 = const()[name = tensor("reshape_47_shape_0"), val = tensor([-1])]; + tensor reshape_47 = reshape(shape = reshape_47_shape_0, x = var_2162)[name = tensor("reshape_47")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([-1])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = reshape_44)[name = tensor("reshape_48")]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9_validate_indices_0 = const()[name = tensor("scatter_9_validate_indices_0"), val = tensor(false)]; + tensor scatter_9 = scatter(axis = scatter_9_axis_0, data = reshape_48, indices = reshape_46, mode = scatter_9_mode_0, updates = reshape_47, validate_indices = scatter_9_validate_indices_0)[name = tensor("scatter_9")]; + tensor new_cache_9_internal_tensor_assign_2 = reshape(shape = shape_57, x = scatter_9)[name = tensor("reshape_49")]; + tensor keys_25_begin_0 = const()[name = tensor("keys_25_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_25_end_0 = const()[name = tensor("keys_25_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_25_end_mask_0 = const()[name = tensor("keys_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_25_squeeze_mask_0 = const()[name = tensor("keys_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_25 = slice_by_index(begin = keys_25_begin_0, end = keys_25_end_0, end_mask = keys_25_end_mask_0, squeeze_mask = keys_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("keys_25")]; + tensor values_25_begin_0 = const()[name = tensor("values_25_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_25_end_0 = const()[name = tensor("values_25_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_25_end_mask_0 = const()[name = tensor("values_25_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_25_squeeze_mask_0 = const()[name = tensor("values_25_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_25 = slice_by_index(begin = values_25_begin_0, end = values_25_end_0, end_mask = values_25_end_mask_0, squeeze_mask = values_25_squeeze_mask_0, x = new_cache_9_internal_tensor_assign_2)[name = tensor("values_25")]; + tensor var_2174 = not_equal(x = keys_25, y = keys_25)[name = tensor("op_2174")]; + tensor keys_27 = select(a = var_504, b = keys_25, cond = var_2174)[name = tensor("keys_27")]; + tensor var_2182 = not_equal(x = values_25, y = values_25)[name = tensor("op_2182")]; + tensor values_27 = select(a = var_504, b = values_25, cond = var_2182)[name = tensor("values_27")]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([0, 2, 1, 3])]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([1, 1, 1])]; + tensor var_2220 = reshape(shape = var_2219, x = position4)[name = tensor("op_2220")]; + tensor var_2237 = const()[name = tensor("op_2237"), val = tensor(0x1p+0)]; + tensor valid_len_9 = add(x = var_2220, y = var_2237)[name = tensor("valid_len_9")]; + tensor valid_mask_9 = less(x = k_positions_1_promoted, y = valid_len_9)[name = tensor("valid_mask_9")]; + tensor causal_mask_9 = less_equal(x = k_positions_1_promoted, y = var_2220)[name = tensor("causal_mask_9")]; + tensor attn_mask_17 = logical_and(x = valid_mask_9, y = causal_mask_9)[name = tensor("attn_mask_17")]; + tensor attn_mask_19_axes_0 = const()[name = tensor("attn_mask_19_axes_0"), val = tensor([1])]; + tensor attn_mask_19 = expand_dims(axes = attn_mask_19_axes_0, x = attn_mask_17)[name = tensor("attn_mask_19")]; + tensor var_2249 = const()[name = tensor("op_2249"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2255_transpose_x_0 = const()[name = tensor("op_2255_transpose_x_0"), val = tensor(false)]; + tensor var_2255_transpose_y_0 = const()[name = tensor("op_2255_transpose_y_0"), val = tensor(false)]; + tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = keys_27)[name = tensor("transpose_197")]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = q_27)[name = tensor("transpose_198")]; + tensor var_2255 = matmul(transpose_x = var_2255_transpose_x_0, transpose_y = var_2255_transpose_y_0, x = transpose_80, y = transpose_81)[name = tensor("op_2255")]; + tensor attn_weights_25 = mul(x = var_2255, y = var_2249)[name = tensor("attn_weights_25")]; + tensor var_2257 = logical_not(x = attn_mask_19)[name = tensor("op_2257")]; + tensor var_2258 = const()[name = tensor("op_2258"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_27 = select(a = var_2258, b = attn_weights_25, cond = var_2257)[name = tensor("attn_weights_27")]; + tensor var_2260 = const()[name = tensor("op_2260"), val = tensor(-1)]; + tensor attn_weights_29 = softmax(axis = var_2260, x = attn_weights_27)[name = tensor("attn_weights_29")]; + tensor attn_output_9_transpose_x_0 = const()[name = tensor("attn_output_9_transpose_x_0"), val = tensor(false)]; + tensor attn_output_9_transpose_y_0 = const()[name = tensor("attn_output_9_transpose_y_0"), val = tensor(false)]; + tensor values_29 = transpose(perm = var_2206, x = values_27)[name = tensor("transpose_199")]; + tensor attn_output_9 = matmul(transpose_x = attn_output_9_transpose_x_0, transpose_y = attn_output_9_transpose_y_0, x = attn_weights_29, y = values_29)[name = tensor("attn_output_9")]; + tensor var_2268 = const()[name = tensor("op_2268"), val = tensor([0, 2, 1, 3])]; + tensor var_2271 = const()[name = tensor("op_2271"), val = tensor([1, 1, 1024])]; + tensor var_2269 = transpose(perm = var_2268, x = attn_output_9)[name = tensor("transpose_196")]; + tensor input_45 = reshape(shape = var_2271, x = var_2269)[name = tensor("input_45")]; + tensor attn_out_9 = linear(bias = linear_0_bias_0, weight = attn4_out_proj_weight, x = input_45)[name = tensor("linear_18")]; + tensor var_2277 = const()[name = tensor("op_2277"), val = tensor(0x1p+0)]; + tensor var_2278 = add(x = position4, y = var_2277)[name = tensor("op_2278")]; + tensor input_47 = add(x = input_43, y = attn_out_9)[name = tensor("input_47")]; + tensor var_2282 = const()[name = tensor("op_2282"), val = tensor(0x1.4f8b58p-17)]; + tensor input_49_axes_0 = const()[name = tensor("input_49_axes_0"), val = tensor([-1])]; + tensor input_49 = layer_norm(axes = input_49_axes_0, beta = norm4_2_bias, epsilon = var_2282, gamma = norm4_2_weight, x = input_47)[name = tensor("input_49")]; + tensor var_2290 = linear(bias = linear_3_bias_0, weight = linear4_1_weight, x = input_49)[name = tensor("linear_19")]; + tensor input_51_mode_0 = const()[name = tensor("input_51_mode_0"), val = tensor("EXACT")]; + tensor input_51 = gelu(mode = input_51_mode_0, x = var_2290)[name = tensor("input_51")]; + tensor ffn_out_9 = linear(bias = linear_0_bias_0, weight = linear4_2_weight, x = input_51)[name = tensor("linear_20")]; + tensor input_53 = add(x = input_47, y = ffn_out_9)[name = tensor("input_53")]; + tensor var_2299 = const()[name = tensor("op_2299"), val = tensor(0x1.4f8b58p-17)]; + tensor x_11_axes_0 = const()[name = tensor("x_11_axes_0"), val = tensor([-1])]; + tensor x_11 = layer_norm(axes = x_11_axes_0, beta = norm5_1_bias, epsilon = var_2299, gamma = norm5_1_weight, x = input_53)[name = tensor("x_11")]; + tensor var_2331 = linear(bias = linear_1_bias_0, weight = attn5_in_proj_weight, x = x_11)[name = tensor("linear_21")]; + tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_11 = reshape(shape = var_2335, x = var_2331)[name = tensor("qkv_11")]; + tensor q_31_begin_0 = const()[name = tensor("q_31_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_31_end_0 = const()[name = tensor("q_31_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_31_end_mask_0 = const()[name = tensor("q_31_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_31_squeeze_mask_0 = const()[name = tensor("q_31_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_31 = slice_by_index(begin = q_31_begin_0, end = q_31_end_0, end_mask = q_31_end_mask_0, squeeze_mask = q_31_squeeze_mask_0, x = qkv_11)[name = tensor("q_31")]; + tensor k_21_begin_0 = const()[name = tensor("k_21_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_21_end_0 = const()[name = tensor("k_21_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_21_end_mask_0 = const()[name = tensor("k_21_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_21_squeeze_mask_0 = const()[name = tensor("k_21_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_21 = slice_by_index(begin = k_21_begin_0, end = k_21_end_0, end_mask = k_21_end_mask_0, squeeze_mask = k_21_squeeze_mask_0, x = qkv_11)[name = tensor("k_21")]; + tensor v_11_begin_0 = const()[name = tensor("v_11_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_11_end_0 = const()[name = tensor("v_11_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_11_end_mask_0 = const()[name = tensor("v_11_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_11_squeeze_mask_0 = const()[name = tensor("v_11_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_11 = slice_by_index(begin = v_11_begin_0, end = v_11_end_0, end_mask = v_11_end_mask_0, squeeze_mask = v_11_squeeze_mask_0, x = qkv_11)[name = tensor("v_11")]; + tensor freqs_11 = const()[name = tensor("freqs_11"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210641984)))]; + tensor var_2439 = const()[name = tensor("op_2439"), val = tensor([1, 1, 1, 1])]; + tensor ts_35 = reshape(shape = var_2439, x = position5)[name = tensor("ts_35")]; + tensor var_2443 = const()[name = tensor("op_2443"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_11 = reshape(shape = var_2443, x = q_31)[name = tensor("q_complex_11")]; + tensor var_2447 = const()[name = tensor("op_2447"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_11 = reshape(shape = var_2447, x = k_21)[name = tensor("k_complex_11")]; + tensor var_2451_begin_0 = const()[name = tensor("op_2451_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2451_end_0 = const()[name = tensor("op_2451_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2451_end_mask_0 = const()[name = tensor("op_2451_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2451_squeeze_mask_0 = const()[name = tensor("op_2451_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2451 = slice_by_index(begin = var_2451_begin_0, end = var_2451_end_0, end_mask = var_2451_end_mask_0, squeeze_mask = var_2451_squeeze_mask_0, x = q_complex_11)[name = tensor("op_2451")]; + tensor var_2459_begin_0 = const()[name = tensor("op_2459_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2459_end_0 = const()[name = tensor("op_2459_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2459_end_mask_0 = const()[name = tensor("op_2459_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2459_squeeze_mask_0 = const()[name = tensor("op_2459_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2459 = slice_by_index(begin = var_2459_begin_0, end = var_2459_end_0, end_mask = var_2459_end_mask_0, squeeze_mask = var_2459_squeeze_mask_0, x = q_complex_11)[name = tensor("op_2459")]; + tensor var_2467_begin_0 = const()[name = tensor("op_2467_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2467_end_0 = const()[name = tensor("op_2467_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2467_end_mask_0 = const()[name = tensor("op_2467_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2467_squeeze_mask_0 = const()[name = tensor("op_2467_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2467 = slice_by_index(begin = var_2467_begin_0, end = var_2467_end_0, end_mask = var_2467_end_mask_0, squeeze_mask = var_2467_squeeze_mask_0, x = k_complex_11)[name = tensor("op_2467")]; + tensor var_2475_begin_0 = const()[name = tensor("op_2475_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2475_end_0 = const()[name = tensor("op_2475_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2475_end_mask_0 = const()[name = tensor("op_2475_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2475_squeeze_mask_0 = const()[name = tensor("op_2475_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2475 = slice_by_index(begin = var_2475_begin_0, end = var_2475_end_0, end_mask = var_2475_end_mask_0, squeeze_mask = var_2475_squeeze_mask_0, x = k_complex_11)[name = tensor("op_2475")]; + tensor var_2481 = mul(x = freqs_11, y = ts_35)[name = tensor("op_2481")]; + tensor rotr_11 = cos(x = var_2481)[name = tensor("rotr_11")]; + tensor roti_11 = sin(x = var_2481)[name = tensor("roti_11")]; + tensor var_2485 = mul(x = var_2451, y = rotr_11)[name = tensor("op_2485")]; + tensor var_2486 = mul(x = var_2459, y = roti_11)[name = tensor("op_2486")]; + tensor qor_21 = sub(x = var_2485, y = var_2486)[name = tensor("qor_21")]; + tensor var_2489 = mul(x = var_2451, y = roti_11)[name = tensor("op_2489")]; + tensor var_2490 = mul(x = var_2459, y = rotr_11)[name = tensor("op_2490")]; + tensor qoi_21 = add(x = var_2489, y = var_2490)[name = tensor("qoi_21")]; + tensor var_2493 = mul(x = var_2467, y = rotr_11)[name = tensor("op_2493")]; + tensor var_2494 = mul(x = var_2475, y = roti_11)[name = tensor("op_2494")]; + tensor kor_21 = sub(x = var_2493, y = var_2494)[name = tensor("kor_21")]; + tensor var_2497 = mul(x = var_2467, y = roti_11)[name = tensor("op_2497")]; + tensor var_2498 = mul(x = var_2475, y = rotr_11)[name = tensor("op_2498")]; + tensor koi_21 = add(x = var_2497, y = var_2498)[name = tensor("koi_21")]; + tensor qo_11_axis_0 = const()[name = tensor("qo_11_axis_0"), val = tensor(-1)]; + tensor qo_11 = stack(axis = qo_11_axis_0, values = (qor_21, qoi_21))[name = tensor("qo_11")]; + tensor ko_11_axis_0 = const()[name = tensor("ko_11_axis_0"), val = tensor(-1)]; + tensor ko_11 = stack(axis = ko_11_axis_0, values = (kor_21, koi_21))[name = tensor("ko_11")]; + tensor var_2527 = const()[name = tensor("op_2527"), val = tensor([1, 1, 16, 64])]; + tensor q_33 = reshape(shape = var_2527, x = qo_11)[name = tensor("q_33")]; + tensor var_2529 = const()[name = tensor("op_2529"), val = tensor([1, 1, 16, 64])]; + tensor k_23 = reshape(shape = var_2529, x = ko_11)[name = tensor("k_23")]; + tensor _inversed_2551_y_0 = const()[name = tensor("_inversed_2551_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2551 = mul(x = ts_35, y = _inversed_2551_y_0)[name = tensor("_inversed_2551")]; + tensor var_2552 = floor(x = _inversed_2551)[name = tensor("op_2552")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor(0x1p+9)]; + tensor var_2554 = mul(x = var_2552, y = var_2553)[name = tensor("op_2554")]; + tensor write_indices_float_23 = sub(x = ts_35, y = var_2554)[name = tensor("write_indices_float_23")]; + tensor var_2561_dtype_0 = const()[name = tensor("op_2561_dtype_0"), val = tensor("int32")]; + tensor write_indices_11_reps_0 = const()[name = tensor("write_indices_11_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2561 = cast(dtype = var_2561_dtype_0, x = write_indices_float_23)[name = tensor("cast_450")]; + tensor write_indices_11 = tile(reps = write_indices_11_reps_0, x = var_2561)[name = tensor("write_indices_11")]; + tensor var_2569_begin_0 = const()[name = tensor("op_2569_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2569_end_0 = const()[name = tensor("op_2569_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2569_end_mask_0 = const()[name = tensor("op_2569_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2569_squeeze_mask_0 = const()[name = tensor("op_2569_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2569 = slice_by_index(begin = var_2569_begin_0, end = var_2569_end_0, end_mask = var_2569_end_mask_0, squeeze_mask = var_2569_squeeze_mask_0, x = cache5)[name = tensor("op_2569")]; + tensor var_2571_axis_0 = const()[name = tensor("op_2571_axis_0"), val = tensor(1)]; + tensor var_2571_mode_0 = const()[name = tensor("op_2571_mode_0"), val = tensor("update")]; + tensor var_2571_validate_indices_0 = const()[name = tensor("op_2571_validate_indices_0"), val = tensor(false)]; + tensor var_2571 = scatter_along_axis(axis = var_2571_axis_0, data = var_2569, indices = write_indices_11, mode = var_2571_mode_0, updates = k_23, validate_indices = var_2571_validate_indices_0)[name = tensor("op_2571")]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_38 = const()[name = tensor("concat_38"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_11_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_11_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_58 = const()[name = tensor("shape_58"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_10 = const()[name = tensor("reduce_prod_10"), val = tensor(1048576)]; + tensor range_1d_10_start_0 = const()[name = tensor("range_1d_10_start_0"), val = tensor(0)]; + tensor range_1d_10_step_0 = const()[name = tensor("range_1d_10_step_0"), val = tensor(1)]; + tensor range_1d_10 = range_1d(end = reduce_prod_10, start = range_1d_10_start_0, step = range_1d_10_step_0)[name = tensor("range_1d_10")]; + tensor reshape_50 = reshape(shape = shape_58, x = range_1d_10)[name = tensor("reshape_50")]; + tensor slice_by_index_10 = slice_by_index(begin = concat_37, begin_mask = new_cache_11_internal_tensor_assign_1_begin_mask_0, end = concat_38, end_mask = new_cache_11_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_11_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_11_internal_tensor_assign_1_stride_0, x = reshape_50)[name = tensor("slice_by_index_10")]; + tensor reshape_51_shape_0 = const()[name = tensor("reshape_51_shape_0"), val = tensor([-1])]; + tensor reshape_51 = reshape(shape = reshape_51_shape_0, x = slice_by_index_10)[name = tensor("reshape_51")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([-1])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_2571)[name = tensor("reshape_52")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([-1])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = cache5)[name = tensor("reshape_53")]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10_validate_indices_0 = const()[name = tensor("scatter_10_validate_indices_0"), val = tensor(false)]; + tensor scatter_10 = scatter(axis = scatter_10_axis_0, data = reshape_53, indices = reshape_51, mode = scatter_10_mode_0, updates = reshape_52, validate_indices = scatter_10_validate_indices_0)[name = tensor("scatter_10")]; + tensor reshape_54 = reshape(shape = shape_58, x = scatter_10)[name = tensor("reshape_54")]; + tensor var_2579_begin_0 = const()[name = tensor("op_2579_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2579_end_0 = const()[name = tensor("op_2579_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2579_end_mask_0 = const()[name = tensor("op_2579_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2579_squeeze_mask_0 = const()[name = tensor("op_2579_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2579 = slice_by_index(begin = var_2579_begin_0, end = var_2579_end_0, end_mask = var_2579_end_mask_0, squeeze_mask = var_2579_squeeze_mask_0, x = reshape_54)[name = tensor("op_2579")]; + tensor var_2581_axis_0 = const()[name = tensor("op_2581_axis_0"), val = tensor(1)]; + tensor var_2581_mode_0 = const()[name = tensor("op_2581_mode_0"), val = tensor("update")]; + tensor var_2581_validate_indices_0 = const()[name = tensor("op_2581_validate_indices_0"), val = tensor(false)]; + tensor var_2581 = scatter_along_axis(axis = var_2581_axis_0, data = var_2579, indices = write_indices_11, mode = var_2581_mode_0, updates = v_11, validate_indices = var_2581_validate_indices_0)[name = tensor("op_2581")]; + tensor concat_39 = const()[name = tensor("concat_39"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_40 = const()[name = tensor("concat_40"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_11_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_11_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_11_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_11_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_59 = const()[name = tensor("shape_59"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_11 = const()[name = tensor("reduce_prod_11"), val = tensor(1048576)]; + tensor range_1d_11_start_0 = const()[name = tensor("range_1d_11_start_0"), val = tensor(0)]; + tensor range_1d_11_step_0 = const()[name = tensor("range_1d_11_step_0"), val = tensor(1)]; + tensor range_1d_11 = range_1d(end = reduce_prod_11, start = range_1d_11_start_0, step = range_1d_11_step_0)[name = tensor("range_1d_11")]; + tensor reshape_55 = reshape(shape = shape_59, x = range_1d_11)[name = tensor("reshape_55")]; + tensor slice_by_index_11 = slice_by_index(begin = concat_39, begin_mask = new_cache_11_internal_tensor_assign_2_begin_mask_0, end = concat_40, end_mask = new_cache_11_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_11_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_11_internal_tensor_assign_2_stride_0, x = reshape_55)[name = tensor("slice_by_index_11")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([-1])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = slice_by_index_11)[name = tensor("reshape_56")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([-1])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = var_2581)[name = tensor("reshape_57")]; + tensor reshape_58_shape_0 = const()[name = tensor("reshape_58_shape_0"), val = tensor([-1])]; + tensor reshape_58 = reshape(shape = reshape_58_shape_0, x = reshape_54)[name = tensor("reshape_58")]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11_validate_indices_0 = const()[name = tensor("scatter_11_validate_indices_0"), val = tensor(false)]; + tensor scatter_11 = scatter(axis = scatter_11_axis_0, data = reshape_58, indices = reshape_56, mode = scatter_11_mode_0, updates = reshape_57, validate_indices = scatter_11_validate_indices_0)[name = tensor("scatter_11")]; + tensor new_cache_11_internal_tensor_assign_2 = reshape(shape = shape_59, x = scatter_11)[name = tensor("reshape_59")]; + tensor keys_31_begin_0 = const()[name = tensor("keys_31_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_31_end_0 = const()[name = tensor("keys_31_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_31_end_mask_0 = const()[name = tensor("keys_31_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_31_squeeze_mask_0 = const()[name = tensor("keys_31_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_31 = slice_by_index(begin = keys_31_begin_0, end = keys_31_end_0, end_mask = keys_31_end_mask_0, squeeze_mask = keys_31_squeeze_mask_0, x = new_cache_11_internal_tensor_assign_2)[name = tensor("keys_31")]; + tensor values_31_begin_0 = const()[name = tensor("values_31_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_31_end_0 = const()[name = tensor("values_31_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_31_end_mask_0 = const()[name = tensor("values_31_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_31_squeeze_mask_0 = const()[name = tensor("values_31_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_31 = slice_by_index(begin = values_31_begin_0, end = values_31_end_0, end_mask = values_31_end_mask_0, squeeze_mask = values_31_squeeze_mask_0, x = new_cache_11_internal_tensor_assign_2)[name = tensor("values_31")]; + tensor var_2593 = not_equal(x = keys_31, y = keys_31)[name = tensor("op_2593")]; + tensor keys_33 = select(a = var_504, b = keys_31, cond = var_2593)[name = tensor("keys_33")]; + tensor var_2601 = not_equal(x = values_31, y = values_31)[name = tensor("op_2601")]; + tensor values_33 = select(a = var_504, b = values_31, cond = var_2601)[name = tensor("values_33")]; + tensor var_2625 = const()[name = tensor("op_2625"), val = tensor([0, 2, 1, 3])]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([1, 1, 1])]; + tensor var_2639 = reshape(shape = var_2638, x = position5)[name = tensor("op_2639")]; + tensor var_2656 = const()[name = tensor("op_2656"), val = tensor(0x1p+0)]; + tensor valid_len_11 = add(x = var_2639, y = var_2656)[name = tensor("valid_len_11")]; + tensor valid_mask_11 = less(x = k_positions_1_promoted, y = valid_len_11)[name = tensor("valid_mask_11")]; + tensor causal_mask_11 = less_equal(x = k_positions_1_promoted, y = var_2639)[name = tensor("causal_mask_11")]; + tensor attn_mask_21 = logical_and(x = valid_mask_11, y = causal_mask_11)[name = tensor("attn_mask_21")]; + tensor attn_mask_23_axes_0 = const()[name = tensor("attn_mask_23_axes_0"), val = tensor([1])]; + tensor attn_mask_23 = expand_dims(axes = attn_mask_23_axes_0, x = attn_mask_21)[name = tensor("attn_mask_23")]; + tensor var_2668 = const()[name = tensor("op_2668"), val = tensor([0x1.fffe5cp-4])]; + tensor var_2674_transpose_x_0 = const()[name = tensor("op_2674_transpose_x_0"), val = tensor(false)]; + tensor var_2674_transpose_y_0 = const()[name = tensor("op_2674_transpose_y_0"), val = tensor(false)]; + tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = keys_33)[name = tensor("transpose_193")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = q_33)[name = tensor("transpose_194")]; + tensor var_2674 = matmul(transpose_x = var_2674_transpose_x_0, transpose_y = var_2674_transpose_y_0, x = transpose_82, y = transpose_83)[name = tensor("op_2674")]; + tensor attn_weights_31 = mul(x = var_2674, y = var_2668)[name = tensor("attn_weights_31")]; + tensor var_2676 = logical_not(x = attn_mask_23)[name = tensor("op_2676")]; + tensor var_2677 = const()[name = tensor("op_2677"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_33 = select(a = var_2677, b = attn_weights_31, cond = var_2676)[name = tensor("attn_weights_33")]; + tensor var_2679 = const()[name = tensor("op_2679"), val = tensor(-1)]; + tensor attn_weights_35 = softmax(axis = var_2679, x = attn_weights_33)[name = tensor("attn_weights_35")]; + tensor attn_output_11_transpose_x_0 = const()[name = tensor("attn_output_11_transpose_x_0"), val = tensor(false)]; + tensor attn_output_11_transpose_y_0 = const()[name = tensor("attn_output_11_transpose_y_0"), val = tensor(false)]; + tensor values_35 = transpose(perm = var_2625, x = values_33)[name = tensor("transpose_195")]; + tensor attn_output_11 = matmul(transpose_x = attn_output_11_transpose_x_0, transpose_y = attn_output_11_transpose_y_0, x = attn_weights_35, y = values_35)[name = tensor("attn_output_11")]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([0, 2, 1, 3])]; + tensor var_2690 = const()[name = tensor("op_2690"), val = tensor([1, 1, 1024])]; + tensor var_2688 = transpose(perm = var_2687, x = attn_output_11)[name = tensor("transpose_192")]; + tensor input_55 = reshape(shape = var_2690, x = var_2688)[name = tensor("input_55")]; + tensor attn_out_11 = linear(bias = linear_0_bias_0, weight = attn5_out_proj_weight, x = input_55)[name = tensor("linear_22")]; + tensor var_2696 = const()[name = tensor("op_2696"), val = tensor(0x1p+0)]; + tensor var_2697 = add(x = position5, y = var_2696)[name = tensor("op_2697")]; + tensor input_57 = add(x = input_53, y = attn_out_11)[name = tensor("input_57")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor(0x1.4f8b58p-17)]; + tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; + tensor input_59 = layer_norm(axes = input_59_axes_0, beta = norm5_2_bias, epsilon = var_2701, gamma = norm5_2_weight, x = input_57)[name = tensor("input_59")]; + tensor var_2709 = linear(bias = linear_3_bias_0, weight = linear5_1_weight, x = input_59)[name = tensor("linear_23")]; + tensor input_61_mode_0 = const()[name = tensor("input_61_mode_0"), val = tensor("EXACT")]; + tensor input_61 = gelu(mode = input_61_mode_0, x = var_2709)[name = tensor("input_61")]; + tensor ffn_out_11 = linear(bias = linear_0_bias_0, weight = linear5_2_weight, x = input_61)[name = tensor("linear_24")]; + tensor input_63 = add(x = input_57, y = ffn_out_11)[name = tensor("input_63")]; + tensor var_2718 = const()[name = tensor("op_2718"), val = tensor(0x1.4f8b58p-17)]; + tensor x_13_axes_0 = const()[name = tensor("x_13_axes_0"), val = tensor([-1])]; + tensor x_13 = layer_norm(axes = x_13_axes_0, beta = norm6_1_bias, epsilon = var_2718, gamma = norm6_1_weight, x = input_63)[name = tensor("x_13")]; + tensor var_2750 = linear(bias = linear_1_bias_0, weight = attn6_in_proj_weight, x = x_13)[name = tensor("linear_25")]; + tensor var_2754 = const()[name = tensor("op_2754"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_13 = reshape(shape = var_2754, x = var_2750)[name = tensor("qkv_13")]; + tensor q_37_begin_0 = const()[name = tensor("q_37_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_37_end_0 = const()[name = tensor("q_37_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_37_end_mask_0 = const()[name = tensor("q_37_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_37_squeeze_mask_0 = const()[name = tensor("q_37_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_37 = slice_by_index(begin = q_37_begin_0, end = q_37_end_0, end_mask = q_37_end_mask_0, squeeze_mask = q_37_squeeze_mask_0, x = qkv_13)[name = tensor("q_37")]; + tensor k_25_begin_0 = const()[name = tensor("k_25_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_25_end_0 = const()[name = tensor("k_25_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_25_end_mask_0 = const()[name = tensor("k_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_25_squeeze_mask_0 = const()[name = tensor("k_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_25 = slice_by_index(begin = k_25_begin_0, end = k_25_end_0, end_mask = k_25_end_mask_0, squeeze_mask = k_25_squeeze_mask_0, x = qkv_13)[name = tensor("k_25")]; + tensor v_13_begin_0 = const()[name = tensor("v_13_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_13_end_0 = const()[name = tensor("v_13_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_13_end_mask_0 = const()[name = tensor("v_13_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_13_squeeze_mask_0 = const()[name = tensor("v_13_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_13 = slice_by_index(begin = v_13_begin_0, end = v_13_end_0, end_mask = v_13_end_mask_0, squeeze_mask = v_13_squeeze_mask_0, x = qkv_13)[name = tensor("v_13")]; + tensor freqs_13 = const()[name = tensor("freqs_13"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210642176)))]; + tensor var_2858 = const()[name = tensor("op_2858"), val = tensor([1, 1, 1, 1])]; + tensor ts_41 = reshape(shape = var_2858, x = position6)[name = tensor("ts_41")]; + tensor var_2862 = const()[name = tensor("op_2862"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_13 = reshape(shape = var_2862, x = q_37)[name = tensor("q_complex_13")]; + tensor var_2866 = const()[name = tensor("op_2866"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_13 = reshape(shape = var_2866, x = k_25)[name = tensor("k_complex_13")]; + tensor var_2870_begin_0 = const()[name = tensor("op_2870_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2870_end_0 = const()[name = tensor("op_2870_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2870_end_mask_0 = const()[name = tensor("op_2870_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2870_squeeze_mask_0 = const()[name = tensor("op_2870_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2870 = slice_by_index(begin = var_2870_begin_0, end = var_2870_end_0, end_mask = var_2870_end_mask_0, squeeze_mask = var_2870_squeeze_mask_0, x = q_complex_13)[name = tensor("op_2870")]; + tensor var_2878_begin_0 = const()[name = tensor("op_2878_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2878_end_0 = const()[name = tensor("op_2878_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2878_end_mask_0 = const()[name = tensor("op_2878_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2878_squeeze_mask_0 = const()[name = tensor("op_2878_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2878 = slice_by_index(begin = var_2878_begin_0, end = var_2878_end_0, end_mask = var_2878_end_mask_0, squeeze_mask = var_2878_squeeze_mask_0, x = q_complex_13)[name = tensor("op_2878")]; + tensor var_2886_begin_0 = const()[name = tensor("op_2886_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2886_end_0 = const()[name = tensor("op_2886_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_2886_end_mask_0 = const()[name = tensor("op_2886_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2886_squeeze_mask_0 = const()[name = tensor("op_2886_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2886 = slice_by_index(begin = var_2886_begin_0, end = var_2886_end_0, end_mask = var_2886_end_mask_0, squeeze_mask = var_2886_squeeze_mask_0, x = k_complex_13)[name = tensor("op_2886")]; + tensor var_2894_begin_0 = const()[name = tensor("op_2894_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_2894_end_0 = const()[name = tensor("op_2894_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_2894_end_mask_0 = const()[name = tensor("op_2894_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_2894_squeeze_mask_0 = const()[name = tensor("op_2894_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_2894 = slice_by_index(begin = var_2894_begin_0, end = var_2894_end_0, end_mask = var_2894_end_mask_0, squeeze_mask = var_2894_squeeze_mask_0, x = k_complex_13)[name = tensor("op_2894")]; + tensor var_2900 = mul(x = freqs_13, y = ts_41)[name = tensor("op_2900")]; + tensor rotr_13 = cos(x = var_2900)[name = tensor("rotr_13")]; + tensor roti_13 = sin(x = var_2900)[name = tensor("roti_13")]; + tensor var_2904 = mul(x = var_2870, y = rotr_13)[name = tensor("op_2904")]; + tensor var_2905 = mul(x = var_2878, y = roti_13)[name = tensor("op_2905")]; + tensor qor_25 = sub(x = var_2904, y = var_2905)[name = tensor("qor_25")]; + tensor var_2908 = mul(x = var_2870, y = roti_13)[name = tensor("op_2908")]; + tensor var_2909 = mul(x = var_2878, y = rotr_13)[name = tensor("op_2909")]; + tensor qoi_25 = add(x = var_2908, y = var_2909)[name = tensor("qoi_25")]; + tensor var_2912 = mul(x = var_2886, y = rotr_13)[name = tensor("op_2912")]; + tensor var_2913 = mul(x = var_2894, y = roti_13)[name = tensor("op_2913")]; + tensor kor_25 = sub(x = var_2912, y = var_2913)[name = tensor("kor_25")]; + tensor var_2916 = mul(x = var_2886, y = roti_13)[name = tensor("op_2916")]; + tensor var_2917 = mul(x = var_2894, y = rotr_13)[name = tensor("op_2917")]; + tensor koi_25 = add(x = var_2916, y = var_2917)[name = tensor("koi_25")]; + tensor qo_13_axis_0 = const()[name = tensor("qo_13_axis_0"), val = tensor(-1)]; + tensor qo_13 = stack(axis = qo_13_axis_0, values = (qor_25, qoi_25))[name = tensor("qo_13")]; + tensor ko_13_axis_0 = const()[name = tensor("ko_13_axis_0"), val = tensor(-1)]; + tensor ko_13 = stack(axis = ko_13_axis_0, values = (kor_25, koi_25))[name = tensor("ko_13")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1, 16, 64])]; + tensor q_39 = reshape(shape = var_2946, x = qo_13)[name = tensor("q_39")]; + tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1, 16, 64])]; + tensor k_27 = reshape(shape = var_2948, x = ko_13)[name = tensor("k_27")]; + tensor _inversed_2970_y_0 = const()[name = tensor("_inversed_2970_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_2970 = mul(x = ts_41, y = _inversed_2970_y_0)[name = tensor("_inversed_2970")]; + tensor var_2971 = floor(x = _inversed_2970)[name = tensor("op_2971")]; + tensor var_2972 = const()[name = tensor("op_2972"), val = tensor(0x1p+9)]; + tensor var_2973 = mul(x = var_2971, y = var_2972)[name = tensor("op_2973")]; + tensor write_indices_float_27 = sub(x = ts_41, y = var_2973)[name = tensor("write_indices_float_27")]; + tensor var_2980_dtype_0 = const()[name = tensor("op_2980_dtype_0"), val = tensor("int32")]; + tensor write_indices_13_reps_0 = const()[name = tensor("write_indices_13_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_2980 = cast(dtype = var_2980_dtype_0, x = write_indices_float_27)[name = tensor("cast_449")]; + tensor write_indices_13 = tile(reps = write_indices_13_reps_0, x = var_2980)[name = tensor("write_indices_13")]; + tensor var_2988_begin_0 = const()[name = tensor("op_2988_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_2988_end_0 = const()[name = tensor("op_2988_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_2988_end_mask_0 = const()[name = tensor("op_2988_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2988_squeeze_mask_0 = const()[name = tensor("op_2988_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2988 = slice_by_index(begin = var_2988_begin_0, end = var_2988_end_0, end_mask = var_2988_end_mask_0, squeeze_mask = var_2988_squeeze_mask_0, x = cache6)[name = tensor("op_2988")]; + tensor var_2990_axis_0 = const()[name = tensor("op_2990_axis_0"), val = tensor(1)]; + tensor var_2990_mode_0 = const()[name = tensor("op_2990_mode_0"), val = tensor("update")]; + tensor var_2990_validate_indices_0 = const()[name = tensor("op_2990_validate_indices_0"), val = tensor(false)]; + tensor var_2990 = scatter_along_axis(axis = var_2990_axis_0, data = var_2988, indices = write_indices_13, mode = var_2990_mode_0, updates = k_27, validate_indices = var_2990_validate_indices_0)[name = tensor("op_2990")]; + tensor concat_44 = const()[name = tensor("concat_44"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_45 = const()[name = tensor("concat_45"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_13_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_13_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_60 = const()[name = tensor("shape_60"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_12 = const()[name = tensor("reduce_prod_12"), val = tensor(1048576)]; + tensor range_1d_12_start_0 = const()[name = tensor("range_1d_12_start_0"), val = tensor(0)]; + tensor range_1d_12_step_0 = const()[name = tensor("range_1d_12_step_0"), val = tensor(1)]; + tensor range_1d_12 = range_1d(end = reduce_prod_12, start = range_1d_12_start_0, step = range_1d_12_step_0)[name = tensor("range_1d_12")]; + tensor reshape_60 = reshape(shape = shape_60, x = range_1d_12)[name = tensor("reshape_60")]; + tensor slice_by_index_12 = slice_by_index(begin = concat_44, begin_mask = new_cache_13_internal_tensor_assign_1_begin_mask_0, end = concat_45, end_mask = new_cache_13_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_13_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_13_internal_tensor_assign_1_stride_0, x = reshape_60)[name = tensor("slice_by_index_12")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([-1])]; + tensor reshape_61 = reshape(shape = reshape_61_shape_0, x = slice_by_index_12)[name = tensor("reshape_61")]; + tensor reshape_62_shape_0 = const()[name = tensor("reshape_62_shape_0"), val = tensor([-1])]; + tensor reshape_62 = reshape(shape = reshape_62_shape_0, x = var_2990)[name = tensor("reshape_62")]; + tensor reshape_63_shape_0 = const()[name = tensor("reshape_63_shape_0"), val = tensor([-1])]; + tensor reshape_63 = reshape(shape = reshape_63_shape_0, x = cache6)[name = tensor("reshape_63")]; + tensor scatter_12_mode_0 = const()[name = tensor("scatter_12_mode_0"), val = tensor("update")]; + tensor scatter_12_axis_0 = const()[name = tensor("scatter_12_axis_0"), val = tensor(0)]; + tensor scatter_12_validate_indices_0 = const()[name = tensor("scatter_12_validate_indices_0"), val = tensor(false)]; + tensor scatter_12 = scatter(axis = scatter_12_axis_0, data = reshape_63, indices = reshape_61, mode = scatter_12_mode_0, updates = reshape_62, validate_indices = scatter_12_validate_indices_0)[name = tensor("scatter_12")]; + tensor reshape_64 = reshape(shape = shape_60, x = scatter_12)[name = tensor("reshape_64")]; + tensor var_2998_begin_0 = const()[name = tensor("op_2998_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_2998_end_0 = const()[name = tensor("op_2998_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_2998_end_mask_0 = const()[name = tensor("op_2998_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_2998_squeeze_mask_0 = const()[name = tensor("op_2998_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_2998 = slice_by_index(begin = var_2998_begin_0, end = var_2998_end_0, end_mask = var_2998_end_mask_0, squeeze_mask = var_2998_squeeze_mask_0, x = reshape_64)[name = tensor("op_2998")]; + tensor var_3000_axis_0 = const()[name = tensor("op_3000_axis_0"), val = tensor(1)]; + tensor var_3000_mode_0 = const()[name = tensor("op_3000_mode_0"), val = tensor("update")]; + tensor var_3000_validate_indices_0 = const()[name = tensor("op_3000_validate_indices_0"), val = tensor(false)]; + tensor var_3000 = scatter_along_axis(axis = var_3000_axis_0, data = var_2998, indices = write_indices_13, mode = var_3000_mode_0, updates = v_13, validate_indices = var_3000_validate_indices_0)[name = tensor("op_3000")]; + tensor concat_46 = const()[name = tensor("concat_46"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_47 = const()[name = tensor("concat_47"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_13_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_13_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_13_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_13_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_61 = const()[name = tensor("shape_61"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_13 = const()[name = tensor("reduce_prod_13"), val = tensor(1048576)]; + tensor range_1d_13_start_0 = const()[name = tensor("range_1d_13_start_0"), val = tensor(0)]; + tensor range_1d_13_step_0 = const()[name = tensor("range_1d_13_step_0"), val = tensor(1)]; + tensor range_1d_13 = range_1d(end = reduce_prod_13, start = range_1d_13_start_0, step = range_1d_13_step_0)[name = tensor("range_1d_13")]; + tensor reshape_65 = reshape(shape = shape_61, x = range_1d_13)[name = tensor("reshape_65")]; + tensor slice_by_index_13 = slice_by_index(begin = concat_46, begin_mask = new_cache_13_internal_tensor_assign_2_begin_mask_0, end = concat_47, end_mask = new_cache_13_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_13_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_13_internal_tensor_assign_2_stride_0, x = reshape_65)[name = tensor("slice_by_index_13")]; + tensor reshape_66_shape_0 = const()[name = tensor("reshape_66_shape_0"), val = tensor([-1])]; + tensor reshape_66 = reshape(shape = reshape_66_shape_0, x = slice_by_index_13)[name = tensor("reshape_66")]; + tensor reshape_67_shape_0 = const()[name = tensor("reshape_67_shape_0"), val = tensor([-1])]; + tensor reshape_67 = reshape(shape = reshape_67_shape_0, x = var_3000)[name = tensor("reshape_67")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([-1])]; + tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = reshape_64)[name = tensor("reshape_68")]; + tensor scatter_13_mode_0 = const()[name = tensor("scatter_13_mode_0"), val = tensor("update")]; + tensor scatter_13_axis_0 = const()[name = tensor("scatter_13_axis_0"), val = tensor(0)]; + tensor scatter_13_validate_indices_0 = const()[name = tensor("scatter_13_validate_indices_0"), val = tensor(false)]; + tensor scatter_13 = scatter(axis = scatter_13_axis_0, data = reshape_68, indices = reshape_66, mode = scatter_13_mode_0, updates = reshape_67, validate_indices = scatter_13_validate_indices_0)[name = tensor("scatter_13")]; + tensor new_cache_13_internal_tensor_assign_2 = reshape(shape = shape_61, x = scatter_13)[name = tensor("reshape_69")]; + tensor keys_37_begin_0 = const()[name = tensor("keys_37_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_37_end_0 = const()[name = tensor("keys_37_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_37_end_mask_0 = const()[name = tensor("keys_37_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_37_squeeze_mask_0 = const()[name = tensor("keys_37_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_37 = slice_by_index(begin = keys_37_begin_0, end = keys_37_end_0, end_mask = keys_37_end_mask_0, squeeze_mask = keys_37_squeeze_mask_0, x = new_cache_13_internal_tensor_assign_2)[name = tensor("keys_37")]; + tensor values_37_begin_0 = const()[name = tensor("values_37_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_37_end_0 = const()[name = tensor("values_37_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_37_end_mask_0 = const()[name = tensor("values_37_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_37_squeeze_mask_0 = const()[name = tensor("values_37_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_37 = slice_by_index(begin = values_37_begin_0, end = values_37_end_0, end_mask = values_37_end_mask_0, squeeze_mask = values_37_squeeze_mask_0, x = new_cache_13_internal_tensor_assign_2)[name = tensor("values_37")]; + tensor var_3012 = not_equal(x = keys_37, y = keys_37)[name = tensor("op_3012")]; + tensor keys_39 = select(a = var_504, b = keys_37, cond = var_3012)[name = tensor("keys_39")]; + tensor var_3020 = not_equal(x = values_37, y = values_37)[name = tensor("op_3020")]; + tensor values_39 = select(a = var_504, b = values_37, cond = var_3020)[name = tensor("values_39")]; + tensor var_3044 = const()[name = tensor("op_3044"), val = tensor([0, 2, 1, 3])]; + tensor var_3057 = const()[name = tensor("op_3057"), val = tensor([1, 1, 1])]; + tensor var_3058 = reshape(shape = var_3057, x = position6)[name = tensor("op_3058")]; + tensor var_3075 = const()[name = tensor("op_3075"), val = tensor(0x1p+0)]; + tensor valid_len_13 = add(x = var_3058, y = var_3075)[name = tensor("valid_len_13")]; + tensor valid_mask_13 = less(x = k_positions_1_promoted, y = valid_len_13)[name = tensor("valid_mask_13")]; + tensor causal_mask_13 = less_equal(x = k_positions_1_promoted, y = var_3058)[name = tensor("causal_mask_13")]; + tensor attn_mask_25 = logical_and(x = valid_mask_13, y = causal_mask_13)[name = tensor("attn_mask_25")]; + tensor attn_mask_27_axes_0 = const()[name = tensor("attn_mask_27_axes_0"), val = tensor([1])]; + tensor attn_mask_27 = expand_dims(axes = attn_mask_27_axes_0, x = attn_mask_25)[name = tensor("attn_mask_27")]; + tensor var_3087 = const()[name = tensor("op_3087"), val = tensor([0x1.fffe5cp-4])]; + tensor var_3093_transpose_x_0 = const()[name = tensor("op_3093_transpose_x_0"), val = tensor(false)]; + tensor var_3093_transpose_y_0 = const()[name = tensor("op_3093_transpose_y_0"), val = tensor(false)]; + tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_85_perm_0 = const()[name = tensor("transpose_85_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = keys_39)[name = tensor("transpose_189")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = q_39)[name = tensor("transpose_190")]; + tensor var_3093 = matmul(transpose_x = var_3093_transpose_x_0, transpose_y = var_3093_transpose_y_0, x = transpose_84, y = transpose_85)[name = tensor("op_3093")]; + tensor attn_weights_37 = mul(x = var_3093, y = var_3087)[name = tensor("attn_weights_37")]; + tensor var_3095 = logical_not(x = attn_mask_27)[name = tensor("op_3095")]; + tensor var_3096 = const()[name = tensor("op_3096"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_39 = select(a = var_3096, b = attn_weights_37, cond = var_3095)[name = tensor("attn_weights_39")]; + tensor var_3098 = const()[name = tensor("op_3098"), val = tensor(-1)]; + tensor attn_weights_41 = softmax(axis = var_3098, x = attn_weights_39)[name = tensor("attn_weights_41")]; + tensor attn_output_13_transpose_x_0 = const()[name = tensor("attn_output_13_transpose_x_0"), val = tensor(false)]; + tensor attn_output_13_transpose_y_0 = const()[name = tensor("attn_output_13_transpose_y_0"), val = tensor(false)]; + tensor values_41 = transpose(perm = var_3044, x = values_39)[name = tensor("transpose_191")]; + tensor attn_output_13 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = attn_weights_41, y = values_41)[name = tensor("attn_output_13")]; + tensor var_3106 = const()[name = tensor("op_3106"), val = tensor([0, 2, 1, 3])]; + tensor var_3109 = const()[name = tensor("op_3109"), val = tensor([1, 1, 1024])]; + tensor var_3107 = transpose(perm = var_3106, x = attn_output_13)[name = tensor("transpose_188")]; + tensor input_65 = reshape(shape = var_3109, x = var_3107)[name = tensor("input_65")]; + tensor attn_out_13 = linear(bias = linear_0_bias_0, weight = attn6_out_proj_weight, x = input_65)[name = tensor("linear_26")]; + tensor var_3115 = const()[name = tensor("op_3115"), val = tensor(0x1p+0)]; + tensor var_3116 = add(x = position6, y = var_3115)[name = tensor("op_3116")]; + tensor input_67 = add(x = input_63, y = attn_out_13)[name = tensor("input_67")]; + tensor var_3120 = const()[name = tensor("op_3120"), val = tensor(0x1.4f8b58p-17)]; + tensor input_69_axes_0 = const()[name = tensor("input_69_axes_0"), val = tensor([-1])]; + tensor input_69 = layer_norm(axes = input_69_axes_0, beta = norm6_2_bias, epsilon = var_3120, gamma = norm6_2_weight, x = input_67)[name = tensor("input_69")]; + tensor var_3128 = linear(bias = linear_3_bias_0, weight = linear6_1_weight, x = input_69)[name = tensor("linear_27")]; + tensor input_71_mode_0 = const()[name = tensor("input_71_mode_0"), val = tensor("EXACT")]; + tensor input_71 = gelu(mode = input_71_mode_0, x = var_3128)[name = tensor("input_71")]; + tensor ffn_out_13 = linear(bias = linear_0_bias_0, weight = linear6_2_weight, x = input_71)[name = tensor("linear_28")]; + tensor input_73 = add(x = input_67, y = ffn_out_13)[name = tensor("input_73")]; + tensor var_3137 = const()[name = tensor("op_3137"), val = tensor(0x1.4f8b58p-17)]; + tensor x_15_axes_0 = const()[name = tensor("x_15_axes_0"), val = tensor([-1])]; + tensor x_15 = layer_norm(axes = x_15_axes_0, beta = norm7_1_bias, epsilon = var_3137, gamma = norm7_1_weight, x = input_73)[name = tensor("x_15")]; + tensor var_3169 = linear(bias = linear_1_bias_0, weight = attn7_in_proj_weight, x = x_15)[name = tensor("linear_29")]; + tensor var_3173 = const()[name = tensor("op_3173"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_15 = reshape(shape = var_3173, x = var_3169)[name = tensor("qkv_15")]; + tensor q_43_begin_0 = const()[name = tensor("q_43_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_43_end_0 = const()[name = tensor("q_43_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_43_end_mask_0 = const()[name = tensor("q_43_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_43_squeeze_mask_0 = const()[name = tensor("q_43_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_43 = slice_by_index(begin = q_43_begin_0, end = q_43_end_0, end_mask = q_43_end_mask_0, squeeze_mask = q_43_squeeze_mask_0, x = qkv_15)[name = tensor("q_43")]; + tensor k_29_begin_0 = const()[name = tensor("k_29_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_29_end_0 = const()[name = tensor("k_29_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_29_end_mask_0 = const()[name = tensor("k_29_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_29_squeeze_mask_0 = const()[name = tensor("k_29_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_29 = slice_by_index(begin = k_29_begin_0, end = k_29_end_0, end_mask = k_29_end_mask_0, squeeze_mask = k_29_squeeze_mask_0, x = qkv_15)[name = tensor("k_29")]; + tensor v_15_begin_0 = const()[name = tensor("v_15_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_15_end_0 = const()[name = tensor("v_15_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_15_end_mask_0 = const()[name = tensor("v_15_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_15_squeeze_mask_0 = const()[name = tensor("v_15_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_15 = slice_by_index(begin = v_15_begin_0, end = v_15_end_0, end_mask = v_15_end_mask_0, squeeze_mask = v_15_squeeze_mask_0, x = qkv_15)[name = tensor("v_15")]; + tensor freqs_15 = const()[name = tensor("freqs_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210642368)))]; + tensor var_3277 = const()[name = tensor("op_3277"), val = tensor([1, 1, 1, 1])]; + tensor ts_47 = reshape(shape = var_3277, x = position7)[name = tensor("ts_47")]; + tensor var_3281 = const()[name = tensor("op_3281"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_15 = reshape(shape = var_3281, x = q_43)[name = tensor("q_complex_15")]; + tensor var_3285 = const()[name = tensor("op_3285"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_15 = reshape(shape = var_3285, x = k_29)[name = tensor("k_complex_15")]; + tensor var_3289_begin_0 = const()[name = tensor("op_3289_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3289_end_0 = const()[name = tensor("op_3289_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3289_end_mask_0 = const()[name = tensor("op_3289_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3289_squeeze_mask_0 = const()[name = tensor("op_3289_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3289 = slice_by_index(begin = var_3289_begin_0, end = var_3289_end_0, end_mask = var_3289_end_mask_0, squeeze_mask = var_3289_squeeze_mask_0, x = q_complex_15)[name = tensor("op_3289")]; + tensor var_3297_begin_0 = const()[name = tensor("op_3297_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3297_end_0 = const()[name = tensor("op_3297_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3297_end_mask_0 = const()[name = tensor("op_3297_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3297_squeeze_mask_0 = const()[name = tensor("op_3297_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3297 = slice_by_index(begin = var_3297_begin_0, end = var_3297_end_0, end_mask = var_3297_end_mask_0, squeeze_mask = var_3297_squeeze_mask_0, x = q_complex_15)[name = tensor("op_3297")]; + tensor var_3305_begin_0 = const()[name = tensor("op_3305_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3305_end_0 = const()[name = tensor("op_3305_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3305_end_mask_0 = const()[name = tensor("op_3305_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3305_squeeze_mask_0 = const()[name = tensor("op_3305_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3305 = slice_by_index(begin = var_3305_begin_0, end = var_3305_end_0, end_mask = var_3305_end_mask_0, squeeze_mask = var_3305_squeeze_mask_0, x = k_complex_15)[name = tensor("op_3305")]; + tensor var_3313_begin_0 = const()[name = tensor("op_3313_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3313_end_0 = const()[name = tensor("op_3313_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3313_end_mask_0 = const()[name = tensor("op_3313_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3313_squeeze_mask_0 = const()[name = tensor("op_3313_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3313 = slice_by_index(begin = var_3313_begin_0, end = var_3313_end_0, end_mask = var_3313_end_mask_0, squeeze_mask = var_3313_squeeze_mask_0, x = k_complex_15)[name = tensor("op_3313")]; + tensor var_3319 = mul(x = freqs_15, y = ts_47)[name = tensor("op_3319")]; + tensor rotr_15 = cos(x = var_3319)[name = tensor("rotr_15")]; + tensor roti_15 = sin(x = var_3319)[name = tensor("roti_15")]; + tensor var_3323 = mul(x = var_3289, y = rotr_15)[name = tensor("op_3323")]; + tensor var_3324 = mul(x = var_3297, y = roti_15)[name = tensor("op_3324")]; + tensor qor_29 = sub(x = var_3323, y = var_3324)[name = tensor("qor_29")]; + tensor var_3327 = mul(x = var_3289, y = roti_15)[name = tensor("op_3327")]; + tensor var_3328 = mul(x = var_3297, y = rotr_15)[name = tensor("op_3328")]; + tensor qoi_29 = add(x = var_3327, y = var_3328)[name = tensor("qoi_29")]; + tensor var_3331 = mul(x = var_3305, y = rotr_15)[name = tensor("op_3331")]; + tensor var_3332 = mul(x = var_3313, y = roti_15)[name = tensor("op_3332")]; + tensor kor_29 = sub(x = var_3331, y = var_3332)[name = tensor("kor_29")]; + tensor var_3335 = mul(x = var_3305, y = roti_15)[name = tensor("op_3335")]; + tensor var_3336 = mul(x = var_3313, y = rotr_15)[name = tensor("op_3336")]; + tensor koi_29 = add(x = var_3335, y = var_3336)[name = tensor("koi_29")]; + tensor qo_15_axis_0 = const()[name = tensor("qo_15_axis_0"), val = tensor(-1)]; + tensor qo_15 = stack(axis = qo_15_axis_0, values = (qor_29, qoi_29))[name = tensor("qo_15")]; + tensor ko_15_axis_0 = const()[name = tensor("ko_15_axis_0"), val = tensor(-1)]; + tensor ko_15 = stack(axis = ko_15_axis_0, values = (kor_29, koi_29))[name = tensor("ko_15")]; + tensor var_3365 = const()[name = tensor("op_3365"), val = tensor([1, 1, 16, 64])]; + tensor q_45 = reshape(shape = var_3365, x = qo_15)[name = tensor("q_45")]; + tensor var_3367 = const()[name = tensor("op_3367"), val = tensor([1, 1, 16, 64])]; + tensor k_31 = reshape(shape = var_3367, x = ko_15)[name = tensor("k_31")]; + tensor _inversed_3389_y_0 = const()[name = tensor("_inversed_3389_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_3389 = mul(x = ts_47, y = _inversed_3389_y_0)[name = tensor("_inversed_3389")]; + tensor var_3390 = floor(x = _inversed_3389)[name = tensor("op_3390")]; + tensor var_3391 = const()[name = tensor("op_3391"), val = tensor(0x1p+9)]; + tensor var_3392 = mul(x = var_3390, y = var_3391)[name = tensor("op_3392")]; + tensor write_indices_float_31 = sub(x = ts_47, y = var_3392)[name = tensor("write_indices_float_31")]; + tensor var_3399_dtype_0 = const()[name = tensor("op_3399_dtype_0"), val = tensor("int32")]; + tensor write_indices_15_reps_0 = const()[name = tensor("write_indices_15_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_3399 = cast(dtype = var_3399_dtype_0, x = write_indices_float_31)[name = tensor("cast_448")]; + tensor write_indices_15 = tile(reps = write_indices_15_reps_0, x = var_3399)[name = tensor("write_indices_15")]; + tensor var_3407_begin_0 = const()[name = tensor("op_3407_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3407_end_0 = const()[name = tensor("op_3407_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_3407_end_mask_0 = const()[name = tensor("op_3407_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3407_squeeze_mask_0 = const()[name = tensor("op_3407_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3407 = slice_by_index(begin = var_3407_begin_0, end = var_3407_end_0, end_mask = var_3407_end_mask_0, squeeze_mask = var_3407_squeeze_mask_0, x = cache7)[name = tensor("op_3407")]; + tensor var_3409_axis_0 = const()[name = tensor("op_3409_axis_0"), val = tensor(1)]; + tensor var_3409_mode_0 = const()[name = tensor("op_3409_mode_0"), val = tensor("update")]; + tensor var_3409_validate_indices_0 = const()[name = tensor("op_3409_validate_indices_0"), val = tensor(false)]; + tensor var_3409 = scatter_along_axis(axis = var_3409_axis_0, data = var_3407, indices = write_indices_15, mode = var_3409_mode_0, updates = k_31, validate_indices = var_3409_validate_indices_0)[name = tensor("op_3409")]; + tensor concat_51 = const()[name = tensor("concat_51"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_52 = const()[name = tensor("concat_52"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_15_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_15_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_62 = const()[name = tensor("shape_62"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_14 = const()[name = tensor("reduce_prod_14"), val = tensor(1048576)]; + tensor range_1d_14_start_0 = const()[name = tensor("range_1d_14_start_0"), val = tensor(0)]; + tensor range_1d_14_step_0 = const()[name = tensor("range_1d_14_step_0"), val = tensor(1)]; + tensor range_1d_14 = range_1d(end = reduce_prod_14, start = range_1d_14_start_0, step = range_1d_14_step_0)[name = tensor("range_1d_14")]; + tensor reshape_70 = reshape(shape = shape_62, x = range_1d_14)[name = tensor("reshape_70")]; + tensor slice_by_index_14 = slice_by_index(begin = concat_51, begin_mask = new_cache_15_internal_tensor_assign_1_begin_mask_0, end = concat_52, end_mask = new_cache_15_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_15_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_15_internal_tensor_assign_1_stride_0, x = reshape_70)[name = tensor("slice_by_index_14")]; + tensor reshape_71_shape_0 = const()[name = tensor("reshape_71_shape_0"), val = tensor([-1])]; + tensor reshape_71 = reshape(shape = reshape_71_shape_0, x = slice_by_index_14)[name = tensor("reshape_71")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([-1])]; + tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = var_3409)[name = tensor("reshape_72")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([-1])]; + tensor reshape_73 = reshape(shape = reshape_73_shape_0, x = cache7)[name = tensor("reshape_73")]; + tensor scatter_14_mode_0 = const()[name = tensor("scatter_14_mode_0"), val = tensor("update")]; + tensor scatter_14_axis_0 = const()[name = tensor("scatter_14_axis_0"), val = tensor(0)]; + tensor scatter_14_validate_indices_0 = const()[name = tensor("scatter_14_validate_indices_0"), val = tensor(false)]; + tensor scatter_14 = scatter(axis = scatter_14_axis_0, data = reshape_73, indices = reshape_71, mode = scatter_14_mode_0, updates = reshape_72, validate_indices = scatter_14_validate_indices_0)[name = tensor("scatter_14")]; + tensor reshape_74 = reshape(shape = shape_62, x = scatter_14)[name = tensor("reshape_74")]; + tensor var_3417_begin_0 = const()[name = tensor("op_3417_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_3417_end_0 = const()[name = tensor("op_3417_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_3417_end_mask_0 = const()[name = tensor("op_3417_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3417_squeeze_mask_0 = const()[name = tensor("op_3417_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3417 = slice_by_index(begin = var_3417_begin_0, end = var_3417_end_0, end_mask = var_3417_end_mask_0, squeeze_mask = var_3417_squeeze_mask_0, x = reshape_74)[name = tensor("op_3417")]; + tensor var_3419_axis_0 = const()[name = tensor("op_3419_axis_0"), val = tensor(1)]; + tensor var_3419_mode_0 = const()[name = tensor("op_3419_mode_0"), val = tensor("update")]; + tensor var_3419_validate_indices_0 = const()[name = tensor("op_3419_validate_indices_0"), val = tensor(false)]; + tensor var_3419 = scatter_along_axis(axis = var_3419_axis_0, data = var_3417, indices = write_indices_15, mode = var_3419_mode_0, updates = v_15, validate_indices = var_3419_validate_indices_0)[name = tensor("op_3419")]; + tensor concat_53 = const()[name = tensor("concat_53"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_54 = const()[name = tensor("concat_54"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_15_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_15_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_15_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_15_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_63 = const()[name = tensor("shape_63"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_15 = const()[name = tensor("reduce_prod_15"), val = tensor(1048576)]; + tensor range_1d_15_start_0 = const()[name = tensor("range_1d_15_start_0"), val = tensor(0)]; + tensor range_1d_15_step_0 = const()[name = tensor("range_1d_15_step_0"), val = tensor(1)]; + tensor range_1d_15 = range_1d(end = reduce_prod_15, start = range_1d_15_start_0, step = range_1d_15_step_0)[name = tensor("range_1d_15")]; + tensor reshape_75 = reshape(shape = shape_63, x = range_1d_15)[name = tensor("reshape_75")]; + tensor slice_by_index_15 = slice_by_index(begin = concat_53, begin_mask = new_cache_15_internal_tensor_assign_2_begin_mask_0, end = concat_54, end_mask = new_cache_15_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_15_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_15_internal_tensor_assign_2_stride_0, x = reshape_75)[name = tensor("slice_by_index_15")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([-1])]; + tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = slice_by_index_15)[name = tensor("reshape_76")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([-1])]; + tensor reshape_77 = reshape(shape = reshape_77_shape_0, x = var_3419)[name = tensor("reshape_77")]; + tensor reshape_78_shape_0 = const()[name = tensor("reshape_78_shape_0"), val = tensor([-1])]; + tensor reshape_78 = reshape(shape = reshape_78_shape_0, x = reshape_74)[name = tensor("reshape_78")]; + tensor scatter_15_mode_0 = const()[name = tensor("scatter_15_mode_0"), val = tensor("update")]; + tensor scatter_15_axis_0 = const()[name = tensor("scatter_15_axis_0"), val = tensor(0)]; + tensor scatter_15_validate_indices_0 = const()[name = tensor("scatter_15_validate_indices_0"), val = tensor(false)]; + tensor scatter_15 = scatter(axis = scatter_15_axis_0, data = reshape_78, indices = reshape_76, mode = scatter_15_mode_0, updates = reshape_77, validate_indices = scatter_15_validate_indices_0)[name = tensor("scatter_15")]; + tensor new_cache_15_internal_tensor_assign_2 = reshape(shape = shape_63, x = scatter_15)[name = tensor("reshape_79")]; + tensor keys_43_begin_0 = const()[name = tensor("keys_43_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_43_end_0 = const()[name = tensor("keys_43_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_43_end_mask_0 = const()[name = tensor("keys_43_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_43_squeeze_mask_0 = const()[name = tensor("keys_43_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_43 = slice_by_index(begin = keys_43_begin_0, end = keys_43_end_0, end_mask = keys_43_end_mask_0, squeeze_mask = keys_43_squeeze_mask_0, x = new_cache_15_internal_tensor_assign_2)[name = tensor("keys_43")]; + tensor values_43_begin_0 = const()[name = tensor("values_43_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_43_end_0 = const()[name = tensor("values_43_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_43_end_mask_0 = const()[name = tensor("values_43_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_43_squeeze_mask_0 = const()[name = tensor("values_43_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_43 = slice_by_index(begin = values_43_begin_0, end = values_43_end_0, end_mask = values_43_end_mask_0, squeeze_mask = values_43_squeeze_mask_0, x = new_cache_15_internal_tensor_assign_2)[name = tensor("values_43")]; + tensor var_3431 = not_equal(x = keys_43, y = keys_43)[name = tensor("op_3431")]; + tensor keys_45 = select(a = var_504, b = keys_43, cond = var_3431)[name = tensor("keys_45")]; + tensor var_3439 = not_equal(x = values_43, y = values_43)[name = tensor("op_3439")]; + tensor values_45 = select(a = var_504, b = values_43, cond = var_3439)[name = tensor("values_45")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([0, 2, 1, 3])]; + tensor var_3476 = const()[name = tensor("op_3476"), val = tensor([1, 1, 1])]; + tensor var_3477 = reshape(shape = var_3476, x = position7)[name = tensor("op_3477")]; + tensor var_3494 = const()[name = tensor("op_3494"), val = tensor(0x1p+0)]; + tensor valid_len_15 = add(x = var_3477, y = var_3494)[name = tensor("valid_len_15")]; + tensor valid_mask_15 = less(x = k_positions_1_promoted, y = valid_len_15)[name = tensor("valid_mask_15")]; + tensor causal_mask_15 = less_equal(x = k_positions_1_promoted, y = var_3477)[name = tensor("causal_mask_15")]; + tensor attn_mask_29 = logical_and(x = valid_mask_15, y = causal_mask_15)[name = tensor("attn_mask_29")]; + tensor attn_mask_31_axes_0 = const()[name = tensor("attn_mask_31_axes_0"), val = tensor([1])]; + tensor attn_mask_31 = expand_dims(axes = attn_mask_31_axes_0, x = attn_mask_29)[name = tensor("attn_mask_31")]; + tensor var_3506 = const()[name = tensor("op_3506"), val = tensor([0x1.fffe5cp-4])]; + tensor var_3512_transpose_x_0 = const()[name = tensor("op_3512_transpose_x_0"), val = tensor(false)]; + tensor var_3512_transpose_y_0 = const()[name = tensor("op_3512_transpose_y_0"), val = tensor(false)]; + tensor transpose_86_perm_0 = const()[name = tensor("transpose_86_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_87_perm_0 = const()[name = tensor("transpose_87_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = keys_45)[name = tensor("transpose_185")]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = q_45)[name = tensor("transpose_186")]; + tensor var_3512 = matmul(transpose_x = var_3512_transpose_x_0, transpose_y = var_3512_transpose_y_0, x = transpose_86, y = transpose_87)[name = tensor("op_3512")]; + tensor attn_weights_43 = mul(x = var_3512, y = var_3506)[name = tensor("attn_weights_43")]; + tensor var_3514 = logical_not(x = attn_mask_31)[name = tensor("op_3514")]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_45 = select(a = var_3515, b = attn_weights_43, cond = var_3514)[name = tensor("attn_weights_45")]; + tensor var_3517 = const()[name = tensor("op_3517"), val = tensor(-1)]; + tensor attn_weights_47 = softmax(axis = var_3517, x = attn_weights_45)[name = tensor("attn_weights_47")]; + tensor attn_output_15_transpose_x_0 = const()[name = tensor("attn_output_15_transpose_x_0"), val = tensor(false)]; + tensor attn_output_15_transpose_y_0 = const()[name = tensor("attn_output_15_transpose_y_0"), val = tensor(false)]; + tensor values_47 = transpose(perm = var_3463, x = values_45)[name = tensor("transpose_187")]; + tensor attn_output_15 = matmul(transpose_x = attn_output_15_transpose_x_0, transpose_y = attn_output_15_transpose_y_0, x = attn_weights_47, y = values_47)[name = tensor("attn_output_15")]; + tensor var_3525 = const()[name = tensor("op_3525"), val = tensor([0, 2, 1, 3])]; + tensor var_3528 = const()[name = tensor("op_3528"), val = tensor([1, 1, 1024])]; + tensor var_3526 = transpose(perm = var_3525, x = attn_output_15)[name = tensor("transpose_184")]; + tensor input_75 = reshape(shape = var_3528, x = var_3526)[name = tensor("input_75")]; + tensor attn_out_15 = linear(bias = linear_0_bias_0, weight = attn7_out_proj_weight, x = input_75)[name = tensor("linear_30")]; + tensor var_3534 = const()[name = tensor("op_3534"), val = tensor(0x1p+0)]; + tensor var_3535 = add(x = position7, y = var_3534)[name = tensor("op_3535")]; + tensor input_77 = add(x = input_73, y = attn_out_15)[name = tensor("input_77")]; + tensor var_3539 = const()[name = tensor("op_3539"), val = tensor(0x1.4f8b58p-17)]; + tensor input_79_axes_0 = const()[name = tensor("input_79_axes_0"), val = tensor([-1])]; + tensor input_79 = layer_norm(axes = input_79_axes_0, beta = norm7_2_bias, epsilon = var_3539, gamma = norm7_2_weight, x = input_77)[name = tensor("input_79")]; + tensor var_3547 = linear(bias = linear_3_bias_0, weight = linear7_1_weight, x = input_79)[name = tensor("linear_31")]; + tensor input_81_mode_0 = const()[name = tensor("input_81_mode_0"), val = tensor("EXACT")]; + tensor input_81 = gelu(mode = input_81_mode_0, x = var_3547)[name = tensor("input_81")]; + tensor ffn_out_15 = linear(bias = linear_0_bias_0, weight = linear7_2_weight, x = input_81)[name = tensor("linear_32")]; + tensor input_83 = add(x = input_77, y = ffn_out_15)[name = tensor("input_83")]; + tensor var_3556 = const()[name = tensor("op_3556"), val = tensor(0x1.4f8b58p-17)]; + tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; + tensor x_17 = layer_norm(axes = x_17_axes_0, beta = norm8_1_bias, epsilon = var_3556, gamma = norm8_1_weight, x = input_83)[name = tensor("x_17")]; + tensor var_3588 = linear(bias = linear_1_bias_0, weight = attn8_in_proj_weight, x = x_17)[name = tensor("linear_33")]; + tensor var_3592 = const()[name = tensor("op_3592"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_17 = reshape(shape = var_3592, x = var_3588)[name = tensor("qkv_17")]; + tensor q_49_begin_0 = const()[name = tensor("q_49_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_49_end_0 = const()[name = tensor("q_49_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_49_end_mask_0 = const()[name = tensor("q_49_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_49_squeeze_mask_0 = const()[name = tensor("q_49_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_49 = slice_by_index(begin = q_49_begin_0, end = q_49_end_0, end_mask = q_49_end_mask_0, squeeze_mask = q_49_squeeze_mask_0, x = qkv_17)[name = tensor("q_49")]; + tensor k_33_begin_0 = const()[name = tensor("k_33_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_33_end_0 = const()[name = tensor("k_33_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_33_end_mask_0 = const()[name = tensor("k_33_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_33_squeeze_mask_0 = const()[name = tensor("k_33_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_33 = slice_by_index(begin = k_33_begin_0, end = k_33_end_0, end_mask = k_33_end_mask_0, squeeze_mask = k_33_squeeze_mask_0, x = qkv_17)[name = tensor("k_33")]; + tensor v_17_begin_0 = const()[name = tensor("v_17_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_17_end_0 = const()[name = tensor("v_17_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_17_end_mask_0 = const()[name = tensor("v_17_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_17_squeeze_mask_0 = const()[name = tensor("v_17_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_17 = slice_by_index(begin = v_17_begin_0, end = v_17_end_0, end_mask = v_17_end_mask_0, squeeze_mask = v_17_squeeze_mask_0, x = qkv_17)[name = tensor("v_17")]; + tensor freqs_17 = const()[name = tensor("freqs_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210642560)))]; + tensor var_3696 = const()[name = tensor("op_3696"), val = tensor([1, 1, 1, 1])]; + tensor ts_53 = reshape(shape = var_3696, x = position8)[name = tensor("ts_53")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_17 = reshape(shape = var_3700, x = q_49)[name = tensor("q_complex_17")]; + tensor var_3704 = const()[name = tensor("op_3704"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_17 = reshape(shape = var_3704, x = k_33)[name = tensor("k_complex_17")]; + tensor var_3708_begin_0 = const()[name = tensor("op_3708_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3708_end_0 = const()[name = tensor("op_3708_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3708_end_mask_0 = const()[name = tensor("op_3708_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3708_squeeze_mask_0 = const()[name = tensor("op_3708_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3708 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, squeeze_mask = var_3708_squeeze_mask_0, x = q_complex_17)[name = tensor("op_3708")]; + tensor var_3716_begin_0 = const()[name = tensor("op_3716_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3716_end_0 = const()[name = tensor("op_3716_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3716_end_mask_0 = const()[name = tensor("op_3716_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3716_squeeze_mask_0 = const()[name = tensor("op_3716_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3716 = slice_by_index(begin = var_3716_begin_0, end = var_3716_end_0, end_mask = var_3716_end_mask_0, squeeze_mask = var_3716_squeeze_mask_0, x = q_complex_17)[name = tensor("op_3716")]; + tensor var_3724_begin_0 = const()[name = tensor("op_3724_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3724_end_0 = const()[name = tensor("op_3724_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_3724_end_mask_0 = const()[name = tensor("op_3724_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3724_squeeze_mask_0 = const()[name = tensor("op_3724_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3724 = slice_by_index(begin = var_3724_begin_0, end = var_3724_end_0, end_mask = var_3724_end_mask_0, squeeze_mask = var_3724_squeeze_mask_0, x = k_complex_17)[name = tensor("op_3724")]; + tensor var_3732_begin_0 = const()[name = tensor("op_3732_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_3732_end_0 = const()[name = tensor("op_3732_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_3732_end_mask_0 = const()[name = tensor("op_3732_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_3732_squeeze_mask_0 = const()[name = tensor("op_3732_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_3732 = slice_by_index(begin = var_3732_begin_0, end = var_3732_end_0, end_mask = var_3732_end_mask_0, squeeze_mask = var_3732_squeeze_mask_0, x = k_complex_17)[name = tensor("op_3732")]; + tensor var_3738 = mul(x = freqs_17, y = ts_53)[name = tensor("op_3738")]; + tensor rotr_17 = cos(x = var_3738)[name = tensor("rotr_17")]; + tensor roti_17 = sin(x = var_3738)[name = tensor("roti_17")]; + tensor var_3742 = mul(x = var_3708, y = rotr_17)[name = tensor("op_3742")]; + tensor var_3743 = mul(x = var_3716, y = roti_17)[name = tensor("op_3743")]; + tensor qor_33 = sub(x = var_3742, y = var_3743)[name = tensor("qor_33")]; + tensor var_3746 = mul(x = var_3708, y = roti_17)[name = tensor("op_3746")]; + tensor var_3747 = mul(x = var_3716, y = rotr_17)[name = tensor("op_3747")]; + tensor qoi_33 = add(x = var_3746, y = var_3747)[name = tensor("qoi_33")]; + tensor var_3750 = mul(x = var_3724, y = rotr_17)[name = tensor("op_3750")]; + tensor var_3751 = mul(x = var_3732, y = roti_17)[name = tensor("op_3751")]; + tensor kor_33 = sub(x = var_3750, y = var_3751)[name = tensor("kor_33")]; + tensor var_3754 = mul(x = var_3724, y = roti_17)[name = tensor("op_3754")]; + tensor var_3755 = mul(x = var_3732, y = rotr_17)[name = tensor("op_3755")]; + tensor koi_33 = add(x = var_3754, y = var_3755)[name = tensor("koi_33")]; + tensor qo_17_axis_0 = const()[name = tensor("qo_17_axis_0"), val = tensor(-1)]; + tensor qo_17 = stack(axis = qo_17_axis_0, values = (qor_33, qoi_33))[name = tensor("qo_17")]; + tensor ko_17_axis_0 = const()[name = tensor("ko_17_axis_0"), val = tensor(-1)]; + tensor ko_17 = stack(axis = ko_17_axis_0, values = (kor_33, koi_33))[name = tensor("ko_17")]; + tensor var_3784 = const()[name = tensor("op_3784"), val = tensor([1, 1, 16, 64])]; + tensor q_51 = reshape(shape = var_3784, x = qo_17)[name = tensor("q_51")]; + tensor var_3786 = const()[name = tensor("op_3786"), val = tensor([1, 1, 16, 64])]; + tensor k_35 = reshape(shape = var_3786, x = ko_17)[name = tensor("k_35")]; + tensor _inversed_3808_y_0 = const()[name = tensor("_inversed_3808_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_3808 = mul(x = ts_53, y = _inversed_3808_y_0)[name = tensor("_inversed_3808")]; + tensor var_3809 = floor(x = _inversed_3808)[name = tensor("op_3809")]; + tensor var_3810 = const()[name = tensor("op_3810"), val = tensor(0x1p+9)]; + tensor var_3811 = mul(x = var_3809, y = var_3810)[name = tensor("op_3811")]; + tensor write_indices_float_35 = sub(x = ts_53, y = var_3811)[name = tensor("write_indices_float_35")]; + tensor var_3818_dtype_0 = const()[name = tensor("op_3818_dtype_0"), val = tensor("int32")]; + tensor write_indices_17_reps_0 = const()[name = tensor("write_indices_17_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_3818 = cast(dtype = var_3818_dtype_0, x = write_indices_float_35)[name = tensor("cast_447")]; + tensor write_indices_17 = tile(reps = write_indices_17_reps_0, x = var_3818)[name = tensor("write_indices_17")]; + tensor var_3826_begin_0 = const()[name = tensor("op_3826_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_3826_end_0 = const()[name = tensor("op_3826_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_3826_end_mask_0 = const()[name = tensor("op_3826_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3826_squeeze_mask_0 = const()[name = tensor("op_3826_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3826 = slice_by_index(begin = var_3826_begin_0, end = var_3826_end_0, end_mask = var_3826_end_mask_0, squeeze_mask = var_3826_squeeze_mask_0, x = cache8)[name = tensor("op_3826")]; + tensor var_3828_axis_0 = const()[name = tensor("op_3828_axis_0"), val = tensor(1)]; + tensor var_3828_mode_0 = const()[name = tensor("op_3828_mode_0"), val = tensor("update")]; + tensor var_3828_validate_indices_0 = const()[name = tensor("op_3828_validate_indices_0"), val = tensor(false)]; + tensor var_3828 = scatter_along_axis(axis = var_3828_axis_0, data = var_3826, indices = write_indices_17, mode = var_3828_mode_0, updates = k_35, validate_indices = var_3828_validate_indices_0)[name = tensor("op_3828")]; + tensor concat_58 = const()[name = tensor("concat_58"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_59 = const()[name = tensor("concat_59"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_17_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_17_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_64 = const()[name = tensor("shape_64"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_16 = const()[name = tensor("reduce_prod_16"), val = tensor(1048576)]; + tensor range_1d_16_start_0 = const()[name = tensor("range_1d_16_start_0"), val = tensor(0)]; + tensor range_1d_16_step_0 = const()[name = tensor("range_1d_16_step_0"), val = tensor(1)]; + tensor range_1d_16 = range_1d(end = reduce_prod_16, start = range_1d_16_start_0, step = range_1d_16_step_0)[name = tensor("range_1d_16")]; + tensor reshape_80 = reshape(shape = shape_64, x = range_1d_16)[name = tensor("reshape_80")]; + tensor slice_by_index_16 = slice_by_index(begin = concat_58, begin_mask = new_cache_17_internal_tensor_assign_1_begin_mask_0, end = concat_59, end_mask = new_cache_17_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_17_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_17_internal_tensor_assign_1_stride_0, x = reshape_80)[name = tensor("slice_by_index_16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([-1])]; + tensor reshape_81 = reshape(shape = reshape_81_shape_0, x = slice_by_index_16)[name = tensor("reshape_81")]; + tensor reshape_82_shape_0 = const()[name = tensor("reshape_82_shape_0"), val = tensor([-1])]; + tensor reshape_82 = reshape(shape = reshape_82_shape_0, x = var_3828)[name = tensor("reshape_82")]; + tensor reshape_83_shape_0 = const()[name = tensor("reshape_83_shape_0"), val = tensor([-1])]; + tensor reshape_83 = reshape(shape = reshape_83_shape_0, x = cache8)[name = tensor("reshape_83")]; + tensor scatter_16_mode_0 = const()[name = tensor("scatter_16_mode_0"), val = tensor("update")]; + tensor scatter_16_axis_0 = const()[name = tensor("scatter_16_axis_0"), val = tensor(0)]; + tensor scatter_16_validate_indices_0 = const()[name = tensor("scatter_16_validate_indices_0"), val = tensor(false)]; + tensor scatter_16 = scatter(axis = scatter_16_axis_0, data = reshape_83, indices = reshape_81, mode = scatter_16_mode_0, updates = reshape_82, validate_indices = scatter_16_validate_indices_0)[name = tensor("scatter_16")]; + tensor reshape_84 = reshape(shape = shape_64, x = scatter_16)[name = tensor("reshape_84")]; + tensor var_3836_begin_0 = const()[name = tensor("op_3836_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_3836_end_0 = const()[name = tensor("op_3836_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_3836_end_mask_0 = const()[name = tensor("op_3836_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_3836_squeeze_mask_0 = const()[name = tensor("op_3836_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_3836 = slice_by_index(begin = var_3836_begin_0, end = var_3836_end_0, end_mask = var_3836_end_mask_0, squeeze_mask = var_3836_squeeze_mask_0, x = reshape_84)[name = tensor("op_3836")]; + tensor var_3838_axis_0 = const()[name = tensor("op_3838_axis_0"), val = tensor(1)]; + tensor var_3838_mode_0 = const()[name = tensor("op_3838_mode_0"), val = tensor("update")]; + tensor var_3838_validate_indices_0 = const()[name = tensor("op_3838_validate_indices_0"), val = tensor(false)]; + tensor var_3838 = scatter_along_axis(axis = var_3838_axis_0, data = var_3836, indices = write_indices_17, mode = var_3838_mode_0, updates = v_17, validate_indices = var_3838_validate_indices_0)[name = tensor("op_3838")]; + tensor concat_60 = const()[name = tensor("concat_60"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_61 = const()[name = tensor("concat_61"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_17_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_17_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_17_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_17_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_65 = const()[name = tensor("shape_65"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_17 = const()[name = tensor("reduce_prod_17"), val = tensor(1048576)]; + tensor range_1d_17_start_0 = const()[name = tensor("range_1d_17_start_0"), val = tensor(0)]; + tensor range_1d_17_step_0 = const()[name = tensor("range_1d_17_step_0"), val = tensor(1)]; + tensor range_1d_17 = range_1d(end = reduce_prod_17, start = range_1d_17_start_0, step = range_1d_17_step_0)[name = tensor("range_1d_17")]; + tensor reshape_85 = reshape(shape = shape_65, x = range_1d_17)[name = tensor("reshape_85")]; + tensor slice_by_index_17 = slice_by_index(begin = concat_60, begin_mask = new_cache_17_internal_tensor_assign_2_begin_mask_0, end = concat_61, end_mask = new_cache_17_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_17_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_17_internal_tensor_assign_2_stride_0, x = reshape_85)[name = tensor("slice_by_index_17")]; + tensor reshape_86_shape_0 = const()[name = tensor("reshape_86_shape_0"), val = tensor([-1])]; + tensor reshape_86 = reshape(shape = reshape_86_shape_0, x = slice_by_index_17)[name = tensor("reshape_86")]; + tensor reshape_87_shape_0 = const()[name = tensor("reshape_87_shape_0"), val = tensor([-1])]; + tensor reshape_87 = reshape(shape = reshape_87_shape_0, x = var_3838)[name = tensor("reshape_87")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([-1])]; + tensor reshape_88 = reshape(shape = reshape_88_shape_0, x = reshape_84)[name = tensor("reshape_88")]; + tensor scatter_17_mode_0 = const()[name = tensor("scatter_17_mode_0"), val = tensor("update")]; + tensor scatter_17_axis_0 = const()[name = tensor("scatter_17_axis_0"), val = tensor(0)]; + tensor scatter_17_validate_indices_0 = const()[name = tensor("scatter_17_validate_indices_0"), val = tensor(false)]; + tensor scatter_17 = scatter(axis = scatter_17_axis_0, data = reshape_88, indices = reshape_86, mode = scatter_17_mode_0, updates = reshape_87, validate_indices = scatter_17_validate_indices_0)[name = tensor("scatter_17")]; + tensor new_cache_17_internal_tensor_assign_2 = reshape(shape = shape_65, x = scatter_17)[name = tensor("reshape_89")]; + tensor keys_49_begin_0 = const()[name = tensor("keys_49_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_49_end_0 = const()[name = tensor("keys_49_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_49_end_mask_0 = const()[name = tensor("keys_49_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_49_squeeze_mask_0 = const()[name = tensor("keys_49_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_49 = slice_by_index(begin = keys_49_begin_0, end = keys_49_end_0, end_mask = keys_49_end_mask_0, squeeze_mask = keys_49_squeeze_mask_0, x = new_cache_17_internal_tensor_assign_2)[name = tensor("keys_49")]; + tensor values_49_begin_0 = const()[name = tensor("values_49_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_49_end_0 = const()[name = tensor("values_49_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_49_end_mask_0 = const()[name = tensor("values_49_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_49_squeeze_mask_0 = const()[name = tensor("values_49_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_49 = slice_by_index(begin = values_49_begin_0, end = values_49_end_0, end_mask = values_49_end_mask_0, squeeze_mask = values_49_squeeze_mask_0, x = new_cache_17_internal_tensor_assign_2)[name = tensor("values_49")]; + tensor var_3850 = not_equal(x = keys_49, y = keys_49)[name = tensor("op_3850")]; + tensor keys_51 = select(a = var_504, b = keys_49, cond = var_3850)[name = tensor("keys_51")]; + tensor var_3858 = not_equal(x = values_49, y = values_49)[name = tensor("op_3858")]; + tensor values_51 = select(a = var_504, b = values_49, cond = var_3858)[name = tensor("values_51")]; + tensor var_3882 = const()[name = tensor("op_3882"), val = tensor([0, 2, 1, 3])]; + tensor var_3895 = const()[name = tensor("op_3895"), val = tensor([1, 1, 1])]; + tensor var_3896 = reshape(shape = var_3895, x = position8)[name = tensor("op_3896")]; + tensor var_3913 = const()[name = tensor("op_3913"), val = tensor(0x1p+0)]; + tensor valid_len_17 = add(x = var_3896, y = var_3913)[name = tensor("valid_len_17")]; + tensor valid_mask_17 = less(x = k_positions_1_promoted, y = valid_len_17)[name = tensor("valid_mask_17")]; + tensor causal_mask_17 = less_equal(x = k_positions_1_promoted, y = var_3896)[name = tensor("causal_mask_17")]; + tensor attn_mask_33 = logical_and(x = valid_mask_17, y = causal_mask_17)[name = tensor("attn_mask_33")]; + tensor attn_mask_35_axes_0 = const()[name = tensor("attn_mask_35_axes_0"), val = tensor([1])]; + tensor attn_mask_35 = expand_dims(axes = attn_mask_35_axes_0, x = attn_mask_33)[name = tensor("attn_mask_35")]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([0x1.fffe5cp-4])]; + tensor var_3931_transpose_x_0 = const()[name = tensor("op_3931_transpose_x_0"), val = tensor(false)]; + tensor var_3931_transpose_y_0 = const()[name = tensor("op_3931_transpose_y_0"), val = tensor(false)]; + tensor transpose_88_perm_0 = const()[name = tensor("transpose_88_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_89_perm_0 = const()[name = tensor("transpose_89_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = keys_51)[name = tensor("transpose_181")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = q_51)[name = tensor("transpose_182")]; + tensor var_3931 = matmul(transpose_x = var_3931_transpose_x_0, transpose_y = var_3931_transpose_y_0, x = transpose_88, y = transpose_89)[name = tensor("op_3931")]; + tensor attn_weights_49 = mul(x = var_3931, y = var_3925)[name = tensor("attn_weights_49")]; + tensor var_3933 = logical_not(x = attn_mask_35)[name = tensor("op_3933")]; + tensor var_3934 = const()[name = tensor("op_3934"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_51 = select(a = var_3934, b = attn_weights_49, cond = var_3933)[name = tensor("attn_weights_51")]; + tensor var_3936 = const()[name = tensor("op_3936"), val = tensor(-1)]; + tensor attn_weights_53 = softmax(axis = var_3936, x = attn_weights_51)[name = tensor("attn_weights_53")]; + tensor attn_output_17_transpose_x_0 = const()[name = tensor("attn_output_17_transpose_x_0"), val = tensor(false)]; + tensor attn_output_17_transpose_y_0 = const()[name = tensor("attn_output_17_transpose_y_0"), val = tensor(false)]; + tensor values_53 = transpose(perm = var_3882, x = values_51)[name = tensor("transpose_183")]; + tensor attn_output_17 = matmul(transpose_x = attn_output_17_transpose_x_0, transpose_y = attn_output_17_transpose_y_0, x = attn_weights_53, y = values_53)[name = tensor("attn_output_17")]; + tensor var_3944 = const()[name = tensor("op_3944"), val = tensor([0, 2, 1, 3])]; + tensor var_3947 = const()[name = tensor("op_3947"), val = tensor([1, 1, 1024])]; + tensor var_3945 = transpose(perm = var_3944, x = attn_output_17)[name = tensor("transpose_180")]; + tensor input_85 = reshape(shape = var_3947, x = var_3945)[name = tensor("input_85")]; + tensor attn_out_17 = linear(bias = linear_0_bias_0, weight = attn8_out_proj_weight, x = input_85)[name = tensor("linear_34")]; + tensor var_3953 = const()[name = tensor("op_3953"), val = tensor(0x1p+0)]; + tensor var_3954 = add(x = position8, y = var_3953)[name = tensor("op_3954")]; + tensor input_87 = add(x = input_83, y = attn_out_17)[name = tensor("input_87")]; + tensor var_3958 = const()[name = tensor("op_3958"), val = tensor(0x1.4f8b58p-17)]; + tensor input_89_axes_0 = const()[name = tensor("input_89_axes_0"), val = tensor([-1])]; + tensor input_89 = layer_norm(axes = input_89_axes_0, beta = norm8_2_bias, epsilon = var_3958, gamma = norm8_2_weight, x = input_87)[name = tensor("input_89")]; + tensor var_3966 = linear(bias = linear_3_bias_0, weight = linear8_1_weight, x = input_89)[name = tensor("linear_35")]; + tensor input_91_mode_0 = const()[name = tensor("input_91_mode_0"), val = tensor("EXACT")]; + tensor input_91 = gelu(mode = input_91_mode_0, x = var_3966)[name = tensor("input_91")]; + tensor ffn_out_17 = linear(bias = linear_0_bias_0, weight = linear8_2_weight, x = input_91)[name = tensor("linear_36")]; + tensor input_93 = add(x = input_87, y = ffn_out_17)[name = tensor("input_93")]; + tensor var_3975 = const()[name = tensor("op_3975"), val = tensor(0x1.4f8b58p-17)]; + tensor x_19_axes_0 = const()[name = tensor("x_19_axes_0"), val = tensor([-1])]; + tensor x_19 = layer_norm(axes = x_19_axes_0, beta = norm9_1_bias, epsilon = var_3975, gamma = norm9_1_weight, x = input_93)[name = tensor("x_19")]; + tensor var_4007 = linear(bias = linear_1_bias_0, weight = attn9_in_proj_weight, x = x_19)[name = tensor("linear_37")]; + tensor var_4011 = const()[name = tensor("op_4011"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_19 = reshape(shape = var_4011, x = var_4007)[name = tensor("qkv_19")]; + tensor q_55_begin_0 = const()[name = tensor("q_55_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_55_end_0 = const()[name = tensor("q_55_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_55_end_mask_0 = const()[name = tensor("q_55_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_55_squeeze_mask_0 = const()[name = tensor("q_55_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_55 = slice_by_index(begin = q_55_begin_0, end = q_55_end_0, end_mask = q_55_end_mask_0, squeeze_mask = q_55_squeeze_mask_0, x = qkv_19)[name = tensor("q_55")]; + tensor k_37_begin_0 = const()[name = tensor("k_37_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_37_end_0 = const()[name = tensor("k_37_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_37_end_mask_0 = const()[name = tensor("k_37_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_37_squeeze_mask_0 = const()[name = tensor("k_37_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_37 = slice_by_index(begin = k_37_begin_0, end = k_37_end_0, end_mask = k_37_end_mask_0, squeeze_mask = k_37_squeeze_mask_0, x = qkv_19)[name = tensor("k_37")]; + tensor v_19_begin_0 = const()[name = tensor("v_19_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_19_end_0 = const()[name = tensor("v_19_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_19_end_mask_0 = const()[name = tensor("v_19_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_19_squeeze_mask_0 = const()[name = tensor("v_19_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_19 = slice_by_index(begin = v_19_begin_0, end = v_19_end_0, end_mask = v_19_end_mask_0, squeeze_mask = v_19_squeeze_mask_0, x = qkv_19)[name = tensor("v_19")]; + tensor freqs_19 = const()[name = tensor("freqs_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210642752)))]; + tensor var_4115 = const()[name = tensor("op_4115"), val = tensor([1, 1, 1, 1])]; + tensor ts_59 = reshape(shape = var_4115, x = position9)[name = tensor("ts_59")]; + tensor var_4119 = const()[name = tensor("op_4119"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_19 = reshape(shape = var_4119, x = q_55)[name = tensor("q_complex_19")]; + tensor var_4123 = const()[name = tensor("op_4123"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_19 = reshape(shape = var_4123, x = k_37)[name = tensor("k_complex_19")]; + tensor var_4127_begin_0 = const()[name = tensor("op_4127_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4127_end_0 = const()[name = tensor("op_4127_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4127_end_mask_0 = const()[name = tensor("op_4127_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4127_squeeze_mask_0 = const()[name = tensor("op_4127_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4127 = slice_by_index(begin = var_4127_begin_0, end = var_4127_end_0, end_mask = var_4127_end_mask_0, squeeze_mask = var_4127_squeeze_mask_0, x = q_complex_19)[name = tensor("op_4127")]; + tensor var_4135_begin_0 = const()[name = tensor("op_4135_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4135_end_0 = const()[name = tensor("op_4135_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4135_end_mask_0 = const()[name = tensor("op_4135_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4135_squeeze_mask_0 = const()[name = tensor("op_4135_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4135 = slice_by_index(begin = var_4135_begin_0, end = var_4135_end_0, end_mask = var_4135_end_mask_0, squeeze_mask = var_4135_squeeze_mask_0, x = q_complex_19)[name = tensor("op_4135")]; + tensor var_4143_begin_0 = const()[name = tensor("op_4143_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4143_end_0 = const()[name = tensor("op_4143_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4143_end_mask_0 = const()[name = tensor("op_4143_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4143_squeeze_mask_0 = const()[name = tensor("op_4143_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4143 = slice_by_index(begin = var_4143_begin_0, end = var_4143_end_0, end_mask = var_4143_end_mask_0, squeeze_mask = var_4143_squeeze_mask_0, x = k_complex_19)[name = tensor("op_4143")]; + tensor var_4151_begin_0 = const()[name = tensor("op_4151_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4151_end_0 = const()[name = tensor("op_4151_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4151_end_mask_0 = const()[name = tensor("op_4151_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4151_squeeze_mask_0 = const()[name = tensor("op_4151_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4151 = slice_by_index(begin = var_4151_begin_0, end = var_4151_end_0, end_mask = var_4151_end_mask_0, squeeze_mask = var_4151_squeeze_mask_0, x = k_complex_19)[name = tensor("op_4151")]; + tensor var_4157 = mul(x = freqs_19, y = ts_59)[name = tensor("op_4157")]; + tensor rotr_19 = cos(x = var_4157)[name = tensor("rotr_19")]; + tensor roti_19 = sin(x = var_4157)[name = tensor("roti_19")]; + tensor var_4161 = mul(x = var_4127, y = rotr_19)[name = tensor("op_4161")]; + tensor var_4162 = mul(x = var_4135, y = roti_19)[name = tensor("op_4162")]; + tensor qor_37 = sub(x = var_4161, y = var_4162)[name = tensor("qor_37")]; + tensor var_4165 = mul(x = var_4127, y = roti_19)[name = tensor("op_4165")]; + tensor var_4166 = mul(x = var_4135, y = rotr_19)[name = tensor("op_4166")]; + tensor qoi_37 = add(x = var_4165, y = var_4166)[name = tensor("qoi_37")]; + tensor var_4169 = mul(x = var_4143, y = rotr_19)[name = tensor("op_4169")]; + tensor var_4170 = mul(x = var_4151, y = roti_19)[name = tensor("op_4170")]; + tensor kor_37 = sub(x = var_4169, y = var_4170)[name = tensor("kor_37")]; + tensor var_4173 = mul(x = var_4143, y = roti_19)[name = tensor("op_4173")]; + tensor var_4174 = mul(x = var_4151, y = rotr_19)[name = tensor("op_4174")]; + tensor koi_37 = add(x = var_4173, y = var_4174)[name = tensor("koi_37")]; + tensor qo_19_axis_0 = const()[name = tensor("qo_19_axis_0"), val = tensor(-1)]; + tensor qo_19 = stack(axis = qo_19_axis_0, values = (qor_37, qoi_37))[name = tensor("qo_19")]; + tensor ko_19_axis_0 = const()[name = tensor("ko_19_axis_0"), val = tensor(-1)]; + tensor ko_19 = stack(axis = ko_19_axis_0, values = (kor_37, koi_37))[name = tensor("ko_19")]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, 1, 16, 64])]; + tensor q_57 = reshape(shape = var_4203, x = qo_19)[name = tensor("q_57")]; + tensor var_4205 = const()[name = tensor("op_4205"), val = tensor([1, 1, 16, 64])]; + tensor k_39 = reshape(shape = var_4205, x = ko_19)[name = tensor("k_39")]; + tensor _inversed_4227_y_0 = const()[name = tensor("_inversed_4227_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_4227 = mul(x = ts_59, y = _inversed_4227_y_0)[name = tensor("_inversed_4227")]; + tensor var_4228 = floor(x = _inversed_4227)[name = tensor("op_4228")]; + tensor var_4229 = const()[name = tensor("op_4229"), val = tensor(0x1p+9)]; + tensor var_4230 = mul(x = var_4228, y = var_4229)[name = tensor("op_4230")]; + tensor write_indices_float_39 = sub(x = ts_59, y = var_4230)[name = tensor("write_indices_float_39")]; + tensor var_4237_dtype_0 = const()[name = tensor("op_4237_dtype_0"), val = tensor("int32")]; + tensor write_indices_19_reps_0 = const()[name = tensor("write_indices_19_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_4237 = cast(dtype = var_4237_dtype_0, x = write_indices_float_39)[name = tensor("cast_446")]; + tensor write_indices_19 = tile(reps = write_indices_19_reps_0, x = var_4237)[name = tensor("write_indices_19")]; + tensor var_4245_begin_0 = const()[name = tensor("op_4245_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4245_end_0 = const()[name = tensor("op_4245_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_4245_end_mask_0 = const()[name = tensor("op_4245_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4245_squeeze_mask_0 = const()[name = tensor("op_4245_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4245 = slice_by_index(begin = var_4245_begin_0, end = var_4245_end_0, end_mask = var_4245_end_mask_0, squeeze_mask = var_4245_squeeze_mask_0, x = cache9)[name = tensor("op_4245")]; + tensor var_4247_axis_0 = const()[name = tensor("op_4247_axis_0"), val = tensor(1)]; + tensor var_4247_mode_0 = const()[name = tensor("op_4247_mode_0"), val = tensor("update")]; + tensor var_4247_validate_indices_0 = const()[name = tensor("op_4247_validate_indices_0"), val = tensor(false)]; + tensor var_4247 = scatter_along_axis(axis = var_4247_axis_0, data = var_4245, indices = write_indices_19, mode = var_4247_mode_0, updates = k_39, validate_indices = var_4247_validate_indices_0)[name = tensor("op_4247")]; + tensor concat_65 = const()[name = tensor("concat_65"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_66 = const()[name = tensor("concat_66"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_19_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_19_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_66 = const()[name = tensor("shape_66"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_18 = const()[name = tensor("reduce_prod_18"), val = tensor(1048576)]; + tensor range_1d_18_start_0 = const()[name = tensor("range_1d_18_start_0"), val = tensor(0)]; + tensor range_1d_18_step_0 = const()[name = tensor("range_1d_18_step_0"), val = tensor(1)]; + tensor range_1d_18 = range_1d(end = reduce_prod_18, start = range_1d_18_start_0, step = range_1d_18_step_0)[name = tensor("range_1d_18")]; + tensor reshape_90 = reshape(shape = shape_66, x = range_1d_18)[name = tensor("reshape_90")]; + tensor slice_by_index_18 = slice_by_index(begin = concat_65, begin_mask = new_cache_19_internal_tensor_assign_1_begin_mask_0, end = concat_66, end_mask = new_cache_19_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_19_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_19_internal_tensor_assign_1_stride_0, x = reshape_90)[name = tensor("slice_by_index_18")]; + tensor reshape_91_shape_0 = const()[name = tensor("reshape_91_shape_0"), val = tensor([-1])]; + tensor reshape_91 = reshape(shape = reshape_91_shape_0, x = slice_by_index_18)[name = tensor("reshape_91")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([-1])]; + tensor reshape_92 = reshape(shape = reshape_92_shape_0, x = var_4247)[name = tensor("reshape_92")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([-1])]; + tensor reshape_93 = reshape(shape = reshape_93_shape_0, x = cache9)[name = tensor("reshape_93")]; + tensor scatter_18_mode_0 = const()[name = tensor("scatter_18_mode_0"), val = tensor("update")]; + tensor scatter_18_axis_0 = const()[name = tensor("scatter_18_axis_0"), val = tensor(0)]; + tensor scatter_18_validate_indices_0 = const()[name = tensor("scatter_18_validate_indices_0"), val = tensor(false)]; + tensor scatter_18 = scatter(axis = scatter_18_axis_0, data = reshape_93, indices = reshape_91, mode = scatter_18_mode_0, updates = reshape_92, validate_indices = scatter_18_validate_indices_0)[name = tensor("scatter_18")]; + tensor reshape_94 = reshape(shape = shape_66, x = scatter_18)[name = tensor("reshape_94")]; + tensor var_4255_begin_0 = const()[name = tensor("op_4255_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_4255_end_0 = const()[name = tensor("op_4255_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_4255_end_mask_0 = const()[name = tensor("op_4255_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4255_squeeze_mask_0 = const()[name = tensor("op_4255_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4255 = slice_by_index(begin = var_4255_begin_0, end = var_4255_end_0, end_mask = var_4255_end_mask_0, squeeze_mask = var_4255_squeeze_mask_0, x = reshape_94)[name = tensor("op_4255")]; + tensor var_4257_axis_0 = const()[name = tensor("op_4257_axis_0"), val = tensor(1)]; + tensor var_4257_mode_0 = const()[name = tensor("op_4257_mode_0"), val = tensor("update")]; + tensor var_4257_validate_indices_0 = const()[name = tensor("op_4257_validate_indices_0"), val = tensor(false)]; + tensor var_4257 = scatter_along_axis(axis = var_4257_axis_0, data = var_4255, indices = write_indices_19, mode = var_4257_mode_0, updates = v_19, validate_indices = var_4257_validate_indices_0)[name = tensor("op_4257")]; + tensor concat_67 = const()[name = tensor("concat_67"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_68 = const()[name = tensor("concat_68"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_19_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_19_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_19_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_19_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_67 = const()[name = tensor("shape_67"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_19 = const()[name = tensor("reduce_prod_19"), val = tensor(1048576)]; + tensor range_1d_19_start_0 = const()[name = tensor("range_1d_19_start_0"), val = tensor(0)]; + tensor range_1d_19_step_0 = const()[name = tensor("range_1d_19_step_0"), val = tensor(1)]; + tensor range_1d_19 = range_1d(end = reduce_prod_19, start = range_1d_19_start_0, step = range_1d_19_step_0)[name = tensor("range_1d_19")]; + tensor reshape_95 = reshape(shape = shape_67, x = range_1d_19)[name = tensor("reshape_95")]; + tensor slice_by_index_19 = slice_by_index(begin = concat_67, begin_mask = new_cache_19_internal_tensor_assign_2_begin_mask_0, end = concat_68, end_mask = new_cache_19_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_19_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_19_internal_tensor_assign_2_stride_0, x = reshape_95)[name = tensor("slice_by_index_19")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([-1])]; + tensor reshape_96 = reshape(shape = reshape_96_shape_0, x = slice_by_index_19)[name = tensor("reshape_96")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([-1])]; + tensor reshape_97 = reshape(shape = reshape_97_shape_0, x = var_4257)[name = tensor("reshape_97")]; + tensor reshape_98_shape_0 = const()[name = tensor("reshape_98_shape_0"), val = tensor([-1])]; + tensor reshape_98 = reshape(shape = reshape_98_shape_0, x = reshape_94)[name = tensor("reshape_98")]; + tensor scatter_19_mode_0 = const()[name = tensor("scatter_19_mode_0"), val = tensor("update")]; + tensor scatter_19_axis_0 = const()[name = tensor("scatter_19_axis_0"), val = tensor(0)]; + tensor scatter_19_validate_indices_0 = const()[name = tensor("scatter_19_validate_indices_0"), val = tensor(false)]; + tensor scatter_19 = scatter(axis = scatter_19_axis_0, data = reshape_98, indices = reshape_96, mode = scatter_19_mode_0, updates = reshape_97, validate_indices = scatter_19_validate_indices_0)[name = tensor("scatter_19")]; + tensor new_cache_19_internal_tensor_assign_2 = reshape(shape = shape_67, x = scatter_19)[name = tensor("reshape_99")]; + tensor keys_55_begin_0 = const()[name = tensor("keys_55_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_55_end_0 = const()[name = tensor("keys_55_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_55_end_mask_0 = const()[name = tensor("keys_55_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_55_squeeze_mask_0 = const()[name = tensor("keys_55_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_55 = slice_by_index(begin = keys_55_begin_0, end = keys_55_end_0, end_mask = keys_55_end_mask_0, squeeze_mask = keys_55_squeeze_mask_0, x = new_cache_19_internal_tensor_assign_2)[name = tensor("keys_55")]; + tensor values_55_begin_0 = const()[name = tensor("values_55_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_55_end_0 = const()[name = tensor("values_55_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_55_end_mask_0 = const()[name = tensor("values_55_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_55_squeeze_mask_0 = const()[name = tensor("values_55_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_55 = slice_by_index(begin = values_55_begin_0, end = values_55_end_0, end_mask = values_55_end_mask_0, squeeze_mask = values_55_squeeze_mask_0, x = new_cache_19_internal_tensor_assign_2)[name = tensor("values_55")]; + tensor var_4269 = not_equal(x = keys_55, y = keys_55)[name = tensor("op_4269")]; + tensor keys_57 = select(a = var_504, b = keys_55, cond = var_4269)[name = tensor("keys_57")]; + tensor var_4277 = not_equal(x = values_55, y = values_55)[name = tensor("op_4277")]; + tensor values_57 = select(a = var_504, b = values_55, cond = var_4277)[name = tensor("values_57")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([0, 2, 1, 3])]; + tensor var_4314 = const()[name = tensor("op_4314"), val = tensor([1, 1, 1])]; + tensor var_4315 = reshape(shape = var_4314, x = position9)[name = tensor("op_4315")]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor(0x1p+0)]; + tensor valid_len_19 = add(x = var_4315, y = var_4332)[name = tensor("valid_len_19")]; + tensor valid_mask_19 = less(x = k_positions_1_promoted, y = valid_len_19)[name = tensor("valid_mask_19")]; + tensor causal_mask_19 = less_equal(x = k_positions_1_promoted, y = var_4315)[name = tensor("causal_mask_19")]; + tensor attn_mask_37 = logical_and(x = valid_mask_19, y = causal_mask_19)[name = tensor("attn_mask_37")]; + tensor attn_mask_39_axes_0 = const()[name = tensor("attn_mask_39_axes_0"), val = tensor([1])]; + tensor attn_mask_39 = expand_dims(axes = attn_mask_39_axes_0, x = attn_mask_37)[name = tensor("attn_mask_39")]; + tensor var_4344 = const()[name = tensor("op_4344"), val = tensor([0x1.fffe5cp-4])]; + tensor var_4350_transpose_x_0 = const()[name = tensor("op_4350_transpose_x_0"), val = tensor(false)]; + tensor var_4350_transpose_y_0 = const()[name = tensor("op_4350_transpose_y_0"), val = tensor(false)]; + tensor transpose_90_perm_0 = const()[name = tensor("transpose_90_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_91_perm_0 = const()[name = tensor("transpose_91_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = keys_57)[name = tensor("transpose_177")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = q_57)[name = tensor("transpose_178")]; + tensor var_4350 = matmul(transpose_x = var_4350_transpose_x_0, transpose_y = var_4350_transpose_y_0, x = transpose_90, y = transpose_91)[name = tensor("op_4350")]; + tensor attn_weights_55 = mul(x = var_4350, y = var_4344)[name = tensor("attn_weights_55")]; + tensor var_4352 = logical_not(x = attn_mask_39)[name = tensor("op_4352")]; + tensor var_4353 = const()[name = tensor("op_4353"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_57 = select(a = var_4353, b = attn_weights_55, cond = var_4352)[name = tensor("attn_weights_57")]; + tensor var_4355 = const()[name = tensor("op_4355"), val = tensor(-1)]; + tensor attn_weights_59 = softmax(axis = var_4355, x = attn_weights_57)[name = tensor("attn_weights_59")]; + tensor attn_output_19_transpose_x_0 = const()[name = tensor("attn_output_19_transpose_x_0"), val = tensor(false)]; + tensor attn_output_19_transpose_y_0 = const()[name = tensor("attn_output_19_transpose_y_0"), val = tensor(false)]; + tensor values_59 = transpose(perm = var_4301, x = values_57)[name = tensor("transpose_179")]; + tensor attn_output_19 = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = attn_weights_59, y = values_59)[name = tensor("attn_output_19")]; + tensor var_4363 = const()[name = tensor("op_4363"), val = tensor([0, 2, 1, 3])]; + tensor var_4366 = const()[name = tensor("op_4366"), val = tensor([1, 1, 1024])]; + tensor var_4364 = transpose(perm = var_4363, x = attn_output_19)[name = tensor("transpose_176")]; + tensor input_95 = reshape(shape = var_4366, x = var_4364)[name = tensor("input_95")]; + tensor attn_out_19 = linear(bias = linear_0_bias_0, weight = attn9_out_proj_weight, x = input_95)[name = tensor("linear_38")]; + tensor var_4372 = const()[name = tensor("op_4372"), val = tensor(0x1p+0)]; + tensor var_4373 = add(x = position9, y = var_4372)[name = tensor("op_4373")]; + tensor input_97 = add(x = input_93, y = attn_out_19)[name = tensor("input_97")]; + tensor var_4377 = const()[name = tensor("op_4377"), val = tensor(0x1.4f8b58p-17)]; + tensor input_99_axes_0 = const()[name = tensor("input_99_axes_0"), val = tensor([-1])]; + tensor input_99 = layer_norm(axes = input_99_axes_0, beta = norm9_2_bias, epsilon = var_4377, gamma = norm9_2_weight, x = input_97)[name = tensor("input_99")]; + tensor var_4385 = linear(bias = linear_3_bias_0, weight = linear9_1_weight, x = input_99)[name = tensor("linear_39")]; + tensor input_101_mode_0 = const()[name = tensor("input_101_mode_0"), val = tensor("EXACT")]; + tensor input_101 = gelu(mode = input_101_mode_0, x = var_4385)[name = tensor("input_101")]; + tensor ffn_out_19 = linear(bias = linear_0_bias_0, weight = linear9_2_weight, x = input_101)[name = tensor("linear_40")]; + tensor input_103 = add(x = input_97, y = ffn_out_19)[name = tensor("input_103")]; + tensor var_4394 = const()[name = tensor("op_4394"), val = tensor(0x1.4f8b58p-17)]; + tensor x_21_axes_0 = const()[name = tensor("x_21_axes_0"), val = tensor([-1])]; + tensor x_21 = layer_norm(axes = x_21_axes_0, beta = norm10_1_bias, epsilon = var_4394, gamma = norm10_1_weight, x = input_103)[name = tensor("x_21")]; + tensor var_4426 = linear(bias = linear_1_bias_0, weight = attn10_in_proj_weight, x = x_21)[name = tensor("linear_41")]; + tensor var_4430 = const()[name = tensor("op_4430"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_21 = reshape(shape = var_4430, x = var_4426)[name = tensor("qkv_21")]; + tensor q_61_begin_0 = const()[name = tensor("q_61_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_61_end_0 = const()[name = tensor("q_61_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_61_end_mask_0 = const()[name = tensor("q_61_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_61_squeeze_mask_0 = const()[name = tensor("q_61_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_61 = slice_by_index(begin = q_61_begin_0, end = q_61_end_0, end_mask = q_61_end_mask_0, squeeze_mask = q_61_squeeze_mask_0, x = qkv_21)[name = tensor("q_61")]; + tensor k_41_begin_0 = const()[name = tensor("k_41_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_41_end_0 = const()[name = tensor("k_41_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_41_end_mask_0 = const()[name = tensor("k_41_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_41_squeeze_mask_0 = const()[name = tensor("k_41_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_41 = slice_by_index(begin = k_41_begin_0, end = k_41_end_0, end_mask = k_41_end_mask_0, squeeze_mask = k_41_squeeze_mask_0, x = qkv_21)[name = tensor("k_41")]; + tensor v_21_begin_0 = const()[name = tensor("v_21_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_21_end_0 = const()[name = tensor("v_21_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_21_end_mask_0 = const()[name = tensor("v_21_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_21_squeeze_mask_0 = const()[name = tensor("v_21_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_21 = slice_by_index(begin = v_21_begin_0, end = v_21_end_0, end_mask = v_21_end_mask_0, squeeze_mask = v_21_squeeze_mask_0, x = qkv_21)[name = tensor("v_21")]; + tensor freqs_21 = const()[name = tensor("freqs_21"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210642944)))]; + tensor var_4534 = const()[name = tensor("op_4534"), val = tensor([1, 1, 1, 1])]; + tensor ts_65 = reshape(shape = var_4534, x = position10)[name = tensor("ts_65")]; + tensor var_4538 = const()[name = tensor("op_4538"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_21 = reshape(shape = var_4538, x = q_61)[name = tensor("q_complex_21")]; + tensor var_4542 = const()[name = tensor("op_4542"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_21 = reshape(shape = var_4542, x = k_41)[name = tensor("k_complex_21")]; + tensor var_4546_begin_0 = const()[name = tensor("op_4546_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4546_end_0 = const()[name = tensor("op_4546_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4546_end_mask_0 = const()[name = tensor("op_4546_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4546_squeeze_mask_0 = const()[name = tensor("op_4546_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4546 = slice_by_index(begin = var_4546_begin_0, end = var_4546_end_0, end_mask = var_4546_end_mask_0, squeeze_mask = var_4546_squeeze_mask_0, x = q_complex_21)[name = tensor("op_4546")]; + tensor var_4554_begin_0 = const()[name = tensor("op_4554_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4554_end_0 = const()[name = tensor("op_4554_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4554_end_mask_0 = const()[name = tensor("op_4554_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4554_squeeze_mask_0 = const()[name = tensor("op_4554_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4554 = slice_by_index(begin = var_4554_begin_0, end = var_4554_end_0, end_mask = var_4554_end_mask_0, squeeze_mask = var_4554_squeeze_mask_0, x = q_complex_21)[name = tensor("op_4554")]; + tensor var_4562_begin_0 = const()[name = tensor("op_4562_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4562_end_0 = const()[name = tensor("op_4562_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4562_end_mask_0 = const()[name = tensor("op_4562_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4562_squeeze_mask_0 = const()[name = tensor("op_4562_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4562 = slice_by_index(begin = var_4562_begin_0, end = var_4562_end_0, end_mask = var_4562_end_mask_0, squeeze_mask = var_4562_squeeze_mask_0, x = k_complex_21)[name = tensor("op_4562")]; + tensor var_4570_begin_0 = const()[name = tensor("op_4570_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4570_end_0 = const()[name = tensor("op_4570_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4570_end_mask_0 = const()[name = tensor("op_4570_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4570_squeeze_mask_0 = const()[name = tensor("op_4570_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4570 = slice_by_index(begin = var_4570_begin_0, end = var_4570_end_0, end_mask = var_4570_end_mask_0, squeeze_mask = var_4570_squeeze_mask_0, x = k_complex_21)[name = tensor("op_4570")]; + tensor var_4576 = mul(x = freqs_21, y = ts_65)[name = tensor("op_4576")]; + tensor rotr_21 = cos(x = var_4576)[name = tensor("rotr_21")]; + tensor roti_21 = sin(x = var_4576)[name = tensor("roti_21")]; + tensor var_4580 = mul(x = var_4546, y = rotr_21)[name = tensor("op_4580")]; + tensor var_4581 = mul(x = var_4554, y = roti_21)[name = tensor("op_4581")]; + tensor qor_41 = sub(x = var_4580, y = var_4581)[name = tensor("qor_41")]; + tensor var_4584 = mul(x = var_4546, y = roti_21)[name = tensor("op_4584")]; + tensor var_4585 = mul(x = var_4554, y = rotr_21)[name = tensor("op_4585")]; + tensor qoi_41 = add(x = var_4584, y = var_4585)[name = tensor("qoi_41")]; + tensor var_4588 = mul(x = var_4562, y = rotr_21)[name = tensor("op_4588")]; + tensor var_4589 = mul(x = var_4570, y = roti_21)[name = tensor("op_4589")]; + tensor kor_41 = sub(x = var_4588, y = var_4589)[name = tensor("kor_41")]; + tensor var_4592 = mul(x = var_4562, y = roti_21)[name = tensor("op_4592")]; + tensor var_4593 = mul(x = var_4570, y = rotr_21)[name = tensor("op_4593")]; + tensor koi_41 = add(x = var_4592, y = var_4593)[name = tensor("koi_41")]; + tensor qo_21_axis_0 = const()[name = tensor("qo_21_axis_0"), val = tensor(-1)]; + tensor qo_21 = stack(axis = qo_21_axis_0, values = (qor_41, qoi_41))[name = tensor("qo_21")]; + tensor ko_21_axis_0 = const()[name = tensor("ko_21_axis_0"), val = tensor(-1)]; + tensor ko_21 = stack(axis = ko_21_axis_0, values = (kor_41, koi_41))[name = tensor("ko_21")]; + tensor var_4622 = const()[name = tensor("op_4622"), val = tensor([1, 1, 16, 64])]; + tensor q_63 = reshape(shape = var_4622, x = qo_21)[name = tensor("q_63")]; + tensor var_4624 = const()[name = tensor("op_4624"), val = tensor([1, 1, 16, 64])]; + tensor k_43 = reshape(shape = var_4624, x = ko_21)[name = tensor("k_43")]; + tensor _inversed_4646_y_0 = const()[name = tensor("_inversed_4646_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_4646 = mul(x = ts_65, y = _inversed_4646_y_0)[name = tensor("_inversed_4646")]; + tensor var_4647 = floor(x = _inversed_4646)[name = tensor("op_4647")]; + tensor var_4648 = const()[name = tensor("op_4648"), val = tensor(0x1p+9)]; + tensor var_4649 = mul(x = var_4647, y = var_4648)[name = tensor("op_4649")]; + tensor write_indices_float_43 = sub(x = ts_65, y = var_4649)[name = tensor("write_indices_float_43")]; + tensor var_4656_dtype_0 = const()[name = tensor("op_4656_dtype_0"), val = tensor("int32")]; + tensor write_indices_21_reps_0 = const()[name = tensor("write_indices_21_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_4656 = cast(dtype = var_4656_dtype_0, x = write_indices_float_43)[name = tensor("cast_445")]; + tensor write_indices_21 = tile(reps = write_indices_21_reps_0, x = var_4656)[name = tensor("write_indices_21")]; + tensor var_4664_begin_0 = const()[name = tensor("op_4664_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4664_end_0 = const()[name = tensor("op_4664_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_4664_end_mask_0 = const()[name = tensor("op_4664_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4664_squeeze_mask_0 = const()[name = tensor("op_4664_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4664 = slice_by_index(begin = var_4664_begin_0, end = var_4664_end_0, end_mask = var_4664_end_mask_0, squeeze_mask = var_4664_squeeze_mask_0, x = cache10)[name = tensor("op_4664")]; + tensor var_4666_axis_0 = const()[name = tensor("op_4666_axis_0"), val = tensor(1)]; + tensor var_4666_mode_0 = const()[name = tensor("op_4666_mode_0"), val = tensor("update")]; + tensor var_4666_validate_indices_0 = const()[name = tensor("op_4666_validate_indices_0"), val = tensor(false)]; + tensor var_4666 = scatter_along_axis(axis = var_4666_axis_0, data = var_4664, indices = write_indices_21, mode = var_4666_mode_0, updates = k_43, validate_indices = var_4666_validate_indices_0)[name = tensor("op_4666")]; + tensor concat_72 = const()[name = tensor("concat_72"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_73 = const()[name = tensor("concat_73"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_21_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_21_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_68 = const()[name = tensor("shape_68"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_20 = const()[name = tensor("reduce_prod_20"), val = tensor(1048576)]; + tensor range_1d_20_start_0 = const()[name = tensor("range_1d_20_start_0"), val = tensor(0)]; + tensor range_1d_20_step_0 = const()[name = tensor("range_1d_20_step_0"), val = tensor(1)]; + tensor range_1d_20 = range_1d(end = reduce_prod_20, start = range_1d_20_start_0, step = range_1d_20_step_0)[name = tensor("range_1d_20")]; + tensor reshape_100 = reshape(shape = shape_68, x = range_1d_20)[name = tensor("reshape_100")]; + tensor slice_by_index_20 = slice_by_index(begin = concat_72, begin_mask = new_cache_21_internal_tensor_assign_1_begin_mask_0, end = concat_73, end_mask = new_cache_21_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_21_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_21_internal_tensor_assign_1_stride_0, x = reshape_100)[name = tensor("slice_by_index_20")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([-1])]; + tensor reshape_101 = reshape(shape = reshape_101_shape_0, x = slice_by_index_20)[name = tensor("reshape_101")]; + tensor reshape_102_shape_0 = const()[name = tensor("reshape_102_shape_0"), val = tensor([-1])]; + tensor reshape_102 = reshape(shape = reshape_102_shape_0, x = var_4666)[name = tensor("reshape_102")]; + tensor reshape_103_shape_0 = const()[name = tensor("reshape_103_shape_0"), val = tensor([-1])]; + tensor reshape_103 = reshape(shape = reshape_103_shape_0, x = cache10)[name = tensor("reshape_103")]; + tensor scatter_20_mode_0 = const()[name = tensor("scatter_20_mode_0"), val = tensor("update")]; + tensor scatter_20_axis_0 = const()[name = tensor("scatter_20_axis_0"), val = tensor(0)]; + tensor scatter_20_validate_indices_0 = const()[name = tensor("scatter_20_validate_indices_0"), val = tensor(false)]; + tensor scatter_20 = scatter(axis = scatter_20_axis_0, data = reshape_103, indices = reshape_101, mode = scatter_20_mode_0, updates = reshape_102, validate_indices = scatter_20_validate_indices_0)[name = tensor("scatter_20")]; + tensor reshape_104 = reshape(shape = shape_68, x = scatter_20)[name = tensor("reshape_104")]; + tensor var_4674_begin_0 = const()[name = tensor("op_4674_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_4674_end_0 = const()[name = tensor("op_4674_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_4674_end_mask_0 = const()[name = tensor("op_4674_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_4674_squeeze_mask_0 = const()[name = tensor("op_4674_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_4674 = slice_by_index(begin = var_4674_begin_0, end = var_4674_end_0, end_mask = var_4674_end_mask_0, squeeze_mask = var_4674_squeeze_mask_0, x = reshape_104)[name = tensor("op_4674")]; + tensor var_4676_axis_0 = const()[name = tensor("op_4676_axis_0"), val = tensor(1)]; + tensor var_4676_mode_0 = const()[name = tensor("op_4676_mode_0"), val = tensor("update")]; + tensor var_4676_validate_indices_0 = const()[name = tensor("op_4676_validate_indices_0"), val = tensor(false)]; + tensor var_4676 = scatter_along_axis(axis = var_4676_axis_0, data = var_4674, indices = write_indices_21, mode = var_4676_mode_0, updates = v_21, validate_indices = var_4676_validate_indices_0)[name = tensor("op_4676")]; + tensor concat_74 = const()[name = tensor("concat_74"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_75 = const()[name = tensor("concat_75"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_21_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_21_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_21_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_21_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_69 = const()[name = tensor("shape_69"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_21 = const()[name = tensor("reduce_prod_21"), val = tensor(1048576)]; + tensor range_1d_21_start_0 = const()[name = tensor("range_1d_21_start_0"), val = tensor(0)]; + tensor range_1d_21_step_0 = const()[name = tensor("range_1d_21_step_0"), val = tensor(1)]; + tensor range_1d_21 = range_1d(end = reduce_prod_21, start = range_1d_21_start_0, step = range_1d_21_step_0)[name = tensor("range_1d_21")]; + tensor reshape_105 = reshape(shape = shape_69, x = range_1d_21)[name = tensor("reshape_105")]; + tensor slice_by_index_21 = slice_by_index(begin = concat_74, begin_mask = new_cache_21_internal_tensor_assign_2_begin_mask_0, end = concat_75, end_mask = new_cache_21_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_21_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_21_internal_tensor_assign_2_stride_0, x = reshape_105)[name = tensor("slice_by_index_21")]; + tensor reshape_106_shape_0 = const()[name = tensor("reshape_106_shape_0"), val = tensor([-1])]; + tensor reshape_106 = reshape(shape = reshape_106_shape_0, x = slice_by_index_21)[name = tensor("reshape_106")]; + tensor reshape_107_shape_0 = const()[name = tensor("reshape_107_shape_0"), val = tensor([-1])]; + tensor reshape_107 = reshape(shape = reshape_107_shape_0, x = var_4676)[name = tensor("reshape_107")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([-1])]; + tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = reshape_104)[name = tensor("reshape_108")]; + tensor scatter_21_mode_0 = const()[name = tensor("scatter_21_mode_0"), val = tensor("update")]; + tensor scatter_21_axis_0 = const()[name = tensor("scatter_21_axis_0"), val = tensor(0)]; + tensor scatter_21_validate_indices_0 = const()[name = tensor("scatter_21_validate_indices_0"), val = tensor(false)]; + tensor scatter_21 = scatter(axis = scatter_21_axis_0, data = reshape_108, indices = reshape_106, mode = scatter_21_mode_0, updates = reshape_107, validate_indices = scatter_21_validate_indices_0)[name = tensor("scatter_21")]; + tensor new_cache_21_internal_tensor_assign_2 = reshape(shape = shape_69, x = scatter_21)[name = tensor("reshape_109")]; + tensor keys_61_begin_0 = const()[name = tensor("keys_61_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_61_end_0 = const()[name = tensor("keys_61_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_61_end_mask_0 = const()[name = tensor("keys_61_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_61_squeeze_mask_0 = const()[name = tensor("keys_61_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_61 = slice_by_index(begin = keys_61_begin_0, end = keys_61_end_0, end_mask = keys_61_end_mask_0, squeeze_mask = keys_61_squeeze_mask_0, x = new_cache_21_internal_tensor_assign_2)[name = tensor("keys_61")]; + tensor values_61_begin_0 = const()[name = tensor("values_61_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_61_end_0 = const()[name = tensor("values_61_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_61_end_mask_0 = const()[name = tensor("values_61_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_61_squeeze_mask_0 = const()[name = tensor("values_61_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_61 = slice_by_index(begin = values_61_begin_0, end = values_61_end_0, end_mask = values_61_end_mask_0, squeeze_mask = values_61_squeeze_mask_0, x = new_cache_21_internal_tensor_assign_2)[name = tensor("values_61")]; + tensor var_4688 = not_equal(x = keys_61, y = keys_61)[name = tensor("op_4688")]; + tensor keys_63 = select(a = var_504, b = keys_61, cond = var_4688)[name = tensor("keys_63")]; + tensor var_4696 = not_equal(x = values_61, y = values_61)[name = tensor("op_4696")]; + tensor values_63 = select(a = var_504, b = values_61, cond = var_4696)[name = tensor("values_63")]; + tensor var_4720 = const()[name = tensor("op_4720"), val = tensor([0, 2, 1, 3])]; + tensor var_4733 = const()[name = tensor("op_4733"), val = tensor([1, 1, 1])]; + tensor var_4734 = reshape(shape = var_4733, x = position10)[name = tensor("op_4734")]; + tensor var_4751 = const()[name = tensor("op_4751"), val = tensor(0x1p+0)]; + tensor valid_len_21 = add(x = var_4734, y = var_4751)[name = tensor("valid_len_21")]; + tensor valid_mask_21 = less(x = k_positions_1_promoted, y = valid_len_21)[name = tensor("valid_mask_21")]; + tensor causal_mask_21 = less_equal(x = k_positions_1_promoted, y = var_4734)[name = tensor("causal_mask_21")]; + tensor attn_mask_41 = logical_and(x = valid_mask_21, y = causal_mask_21)[name = tensor("attn_mask_41")]; + tensor attn_mask_43_axes_0 = const()[name = tensor("attn_mask_43_axes_0"), val = tensor([1])]; + tensor attn_mask_43 = expand_dims(axes = attn_mask_43_axes_0, x = attn_mask_41)[name = tensor("attn_mask_43")]; + tensor var_4763 = const()[name = tensor("op_4763"), val = tensor([0x1.fffe5cp-4])]; + tensor var_4769_transpose_x_0 = const()[name = tensor("op_4769_transpose_x_0"), val = tensor(false)]; + tensor var_4769_transpose_y_0 = const()[name = tensor("op_4769_transpose_y_0"), val = tensor(false)]; + tensor transpose_92_perm_0 = const()[name = tensor("transpose_92_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_93_perm_0 = const()[name = tensor("transpose_93_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = keys_63)[name = tensor("transpose_173")]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = q_63)[name = tensor("transpose_174")]; + tensor var_4769 = matmul(transpose_x = var_4769_transpose_x_0, transpose_y = var_4769_transpose_y_0, x = transpose_92, y = transpose_93)[name = tensor("op_4769")]; + tensor attn_weights_61 = mul(x = var_4769, y = var_4763)[name = tensor("attn_weights_61")]; + tensor var_4771 = logical_not(x = attn_mask_43)[name = tensor("op_4771")]; + tensor var_4772 = const()[name = tensor("op_4772"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_63 = select(a = var_4772, b = attn_weights_61, cond = var_4771)[name = tensor("attn_weights_63")]; + tensor var_4774 = const()[name = tensor("op_4774"), val = tensor(-1)]; + tensor attn_weights_65 = softmax(axis = var_4774, x = attn_weights_63)[name = tensor("attn_weights_65")]; + tensor attn_output_21_transpose_x_0 = const()[name = tensor("attn_output_21_transpose_x_0"), val = tensor(false)]; + tensor attn_output_21_transpose_y_0 = const()[name = tensor("attn_output_21_transpose_y_0"), val = tensor(false)]; + tensor values_65 = transpose(perm = var_4720, x = values_63)[name = tensor("transpose_175")]; + tensor attn_output_21 = matmul(transpose_x = attn_output_21_transpose_x_0, transpose_y = attn_output_21_transpose_y_0, x = attn_weights_65, y = values_65)[name = tensor("attn_output_21")]; + tensor var_4782 = const()[name = tensor("op_4782"), val = tensor([0, 2, 1, 3])]; + tensor var_4785 = const()[name = tensor("op_4785"), val = tensor([1, 1, 1024])]; + tensor var_4783 = transpose(perm = var_4782, x = attn_output_21)[name = tensor("transpose_172")]; + tensor input_105 = reshape(shape = var_4785, x = var_4783)[name = tensor("input_105")]; + tensor attn_out_21 = linear(bias = linear_0_bias_0, weight = attn10_out_proj_weight, x = input_105)[name = tensor("linear_42")]; + tensor var_4791 = const()[name = tensor("op_4791"), val = tensor(0x1p+0)]; + tensor var_4792 = add(x = position10, y = var_4791)[name = tensor("op_4792")]; + tensor input_107 = add(x = input_103, y = attn_out_21)[name = tensor("input_107")]; + tensor var_4796 = const()[name = tensor("op_4796"), val = tensor(0x1.4f8b58p-17)]; + tensor input_109_axes_0 = const()[name = tensor("input_109_axes_0"), val = tensor([-1])]; + tensor input_109 = layer_norm(axes = input_109_axes_0, beta = norm10_2_bias, epsilon = var_4796, gamma = norm10_2_weight, x = input_107)[name = tensor("input_109")]; + tensor var_4804 = linear(bias = linear_3_bias_0, weight = linear10_1_weight, x = input_109)[name = tensor("linear_43")]; + tensor input_111_mode_0 = const()[name = tensor("input_111_mode_0"), val = tensor("EXACT")]; + tensor input_111 = gelu(mode = input_111_mode_0, x = var_4804)[name = tensor("input_111")]; + tensor ffn_out_21 = linear(bias = linear_0_bias_0, weight = linear10_2_weight, x = input_111)[name = tensor("linear_44")]; + tensor input_113 = add(x = input_107, y = ffn_out_21)[name = tensor("input_113")]; + tensor var_4813 = const()[name = tensor("op_4813"), val = tensor(0x1.4f8b58p-17)]; + tensor x_23_axes_0 = const()[name = tensor("x_23_axes_0"), val = tensor([-1])]; + tensor x_23 = layer_norm(axes = x_23_axes_0, beta = norm11_1_bias, epsilon = var_4813, gamma = norm11_1_weight, x = input_113)[name = tensor("x_23")]; + tensor var_4845 = linear(bias = linear_1_bias_0, weight = attn11_in_proj_weight, x = x_23)[name = tensor("linear_45")]; + tensor var_4849 = const()[name = tensor("op_4849"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_23 = reshape(shape = var_4849, x = var_4845)[name = tensor("qkv_23")]; + tensor q_67_begin_0 = const()[name = tensor("q_67_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_67_end_0 = const()[name = tensor("q_67_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_67_end_mask_0 = const()[name = tensor("q_67_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_67_squeeze_mask_0 = const()[name = tensor("q_67_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_67 = slice_by_index(begin = q_67_begin_0, end = q_67_end_0, end_mask = q_67_end_mask_0, squeeze_mask = q_67_squeeze_mask_0, x = qkv_23)[name = tensor("q_67")]; + tensor k_45_begin_0 = const()[name = tensor("k_45_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_45_end_0 = const()[name = tensor("k_45_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_45_end_mask_0 = const()[name = tensor("k_45_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_45_squeeze_mask_0 = const()[name = tensor("k_45_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_45 = slice_by_index(begin = k_45_begin_0, end = k_45_end_0, end_mask = k_45_end_mask_0, squeeze_mask = k_45_squeeze_mask_0, x = qkv_23)[name = tensor("k_45")]; + tensor v_23_begin_0 = const()[name = tensor("v_23_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_23_end_0 = const()[name = tensor("v_23_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_23_end_mask_0 = const()[name = tensor("v_23_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_23_squeeze_mask_0 = const()[name = tensor("v_23_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_23 = slice_by_index(begin = v_23_begin_0, end = v_23_end_0, end_mask = v_23_end_mask_0, squeeze_mask = v_23_squeeze_mask_0, x = qkv_23)[name = tensor("v_23")]; + tensor freqs_23 = const()[name = tensor("freqs_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210643136)))]; + tensor var_4953 = const()[name = tensor("op_4953"), val = tensor([1, 1, 1, 1])]; + tensor ts_71 = reshape(shape = var_4953, x = position11)[name = tensor("ts_71")]; + tensor var_4957 = const()[name = tensor("op_4957"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_23 = reshape(shape = var_4957, x = q_67)[name = tensor("q_complex_23")]; + tensor var_4961 = const()[name = tensor("op_4961"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_23 = reshape(shape = var_4961, x = k_45)[name = tensor("k_complex_23")]; + tensor var_4965_begin_0 = const()[name = tensor("op_4965_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4965_end_0 = const()[name = tensor("op_4965_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4965_end_mask_0 = const()[name = tensor("op_4965_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4965_squeeze_mask_0 = const()[name = tensor("op_4965_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4965 = slice_by_index(begin = var_4965_begin_0, end = var_4965_end_0, end_mask = var_4965_end_mask_0, squeeze_mask = var_4965_squeeze_mask_0, x = q_complex_23)[name = tensor("op_4965")]; + tensor var_4973_begin_0 = const()[name = tensor("op_4973_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4973_end_0 = const()[name = tensor("op_4973_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4973_end_mask_0 = const()[name = tensor("op_4973_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4973_squeeze_mask_0 = const()[name = tensor("op_4973_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4973 = slice_by_index(begin = var_4973_begin_0, end = var_4973_end_0, end_mask = var_4973_end_mask_0, squeeze_mask = var_4973_squeeze_mask_0, x = q_complex_23)[name = tensor("op_4973")]; + tensor var_4981_begin_0 = const()[name = tensor("op_4981_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_4981_end_0 = const()[name = tensor("op_4981_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_4981_end_mask_0 = const()[name = tensor("op_4981_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4981_squeeze_mask_0 = const()[name = tensor("op_4981_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4981 = slice_by_index(begin = var_4981_begin_0, end = var_4981_end_0, end_mask = var_4981_end_mask_0, squeeze_mask = var_4981_squeeze_mask_0, x = k_complex_23)[name = tensor("op_4981")]; + tensor var_4989_begin_0 = const()[name = tensor("op_4989_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_4989_end_0 = const()[name = tensor("op_4989_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_4989_end_mask_0 = const()[name = tensor("op_4989_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_4989_squeeze_mask_0 = const()[name = tensor("op_4989_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_4989 = slice_by_index(begin = var_4989_begin_0, end = var_4989_end_0, end_mask = var_4989_end_mask_0, squeeze_mask = var_4989_squeeze_mask_0, x = k_complex_23)[name = tensor("op_4989")]; + tensor var_4995 = mul(x = freqs_23, y = ts_71)[name = tensor("op_4995")]; + tensor rotr_23 = cos(x = var_4995)[name = tensor("rotr_23")]; + tensor roti_23 = sin(x = var_4995)[name = tensor("roti_23")]; + tensor var_4999 = mul(x = var_4965, y = rotr_23)[name = tensor("op_4999")]; + tensor var_5000 = mul(x = var_4973, y = roti_23)[name = tensor("op_5000")]; + tensor qor_45 = sub(x = var_4999, y = var_5000)[name = tensor("qor_45")]; + tensor var_5003 = mul(x = var_4965, y = roti_23)[name = tensor("op_5003")]; + tensor var_5004 = mul(x = var_4973, y = rotr_23)[name = tensor("op_5004")]; + tensor qoi_45 = add(x = var_5003, y = var_5004)[name = tensor("qoi_45")]; + tensor var_5007 = mul(x = var_4981, y = rotr_23)[name = tensor("op_5007")]; + tensor var_5008 = mul(x = var_4989, y = roti_23)[name = tensor("op_5008")]; + tensor kor_45 = sub(x = var_5007, y = var_5008)[name = tensor("kor_45")]; + tensor var_5011 = mul(x = var_4981, y = roti_23)[name = tensor("op_5011")]; + tensor var_5012 = mul(x = var_4989, y = rotr_23)[name = tensor("op_5012")]; + tensor koi_45 = add(x = var_5011, y = var_5012)[name = tensor("koi_45")]; + tensor qo_23_axis_0 = const()[name = tensor("qo_23_axis_0"), val = tensor(-1)]; + tensor qo_23 = stack(axis = qo_23_axis_0, values = (qor_45, qoi_45))[name = tensor("qo_23")]; + tensor ko_23_axis_0 = const()[name = tensor("ko_23_axis_0"), val = tensor(-1)]; + tensor ko_23 = stack(axis = ko_23_axis_0, values = (kor_45, koi_45))[name = tensor("ko_23")]; + tensor var_5041 = const()[name = tensor("op_5041"), val = tensor([1, 1, 16, 64])]; + tensor q_69 = reshape(shape = var_5041, x = qo_23)[name = tensor("q_69")]; + tensor var_5043 = const()[name = tensor("op_5043"), val = tensor([1, 1, 16, 64])]; + tensor k_47 = reshape(shape = var_5043, x = ko_23)[name = tensor("k_47")]; + tensor _inversed_5065_y_0 = const()[name = tensor("_inversed_5065_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_5065 = mul(x = ts_71, y = _inversed_5065_y_0)[name = tensor("_inversed_5065")]; + tensor var_5066 = floor(x = _inversed_5065)[name = tensor("op_5066")]; + tensor var_5067 = const()[name = tensor("op_5067"), val = tensor(0x1p+9)]; + tensor var_5068 = mul(x = var_5066, y = var_5067)[name = tensor("op_5068")]; + tensor write_indices_float_47 = sub(x = ts_71, y = var_5068)[name = tensor("write_indices_float_47")]; + tensor var_5075_dtype_0 = const()[name = tensor("op_5075_dtype_0"), val = tensor("int32")]; + tensor write_indices_23_reps_0 = const()[name = tensor("write_indices_23_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_5075 = cast(dtype = var_5075_dtype_0, x = write_indices_float_47)[name = tensor("cast_444")]; + tensor write_indices_23 = tile(reps = write_indices_23_reps_0, x = var_5075)[name = tensor("write_indices_23")]; + tensor var_5083_begin_0 = const()[name = tensor("op_5083_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5083_end_0 = const()[name = tensor("op_5083_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_5083_end_mask_0 = const()[name = tensor("op_5083_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5083_squeeze_mask_0 = const()[name = tensor("op_5083_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5083 = slice_by_index(begin = var_5083_begin_0, end = var_5083_end_0, end_mask = var_5083_end_mask_0, squeeze_mask = var_5083_squeeze_mask_0, x = cache11)[name = tensor("op_5083")]; + tensor var_5085_axis_0 = const()[name = tensor("op_5085_axis_0"), val = tensor(1)]; + tensor var_5085_mode_0 = const()[name = tensor("op_5085_mode_0"), val = tensor("update")]; + tensor var_5085_validate_indices_0 = const()[name = tensor("op_5085_validate_indices_0"), val = tensor(false)]; + tensor var_5085 = scatter_along_axis(axis = var_5085_axis_0, data = var_5083, indices = write_indices_23, mode = var_5085_mode_0, updates = k_47, validate_indices = var_5085_validate_indices_0)[name = tensor("op_5085")]; + tensor concat_79 = const()[name = tensor("concat_79"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_80 = const()[name = tensor("concat_80"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_23_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_23_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_70 = const()[name = tensor("shape_70"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_22 = const()[name = tensor("reduce_prod_22"), val = tensor(1048576)]; + tensor range_1d_22_start_0 = const()[name = tensor("range_1d_22_start_0"), val = tensor(0)]; + tensor range_1d_22_step_0 = const()[name = tensor("range_1d_22_step_0"), val = tensor(1)]; + tensor range_1d_22 = range_1d(end = reduce_prod_22, start = range_1d_22_start_0, step = range_1d_22_step_0)[name = tensor("range_1d_22")]; + tensor reshape_110 = reshape(shape = shape_70, x = range_1d_22)[name = tensor("reshape_110")]; + tensor slice_by_index_22 = slice_by_index(begin = concat_79, begin_mask = new_cache_23_internal_tensor_assign_1_begin_mask_0, end = concat_80, end_mask = new_cache_23_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_23_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_23_internal_tensor_assign_1_stride_0, x = reshape_110)[name = tensor("slice_by_index_22")]; + tensor reshape_111_shape_0 = const()[name = tensor("reshape_111_shape_0"), val = tensor([-1])]; + tensor reshape_111 = reshape(shape = reshape_111_shape_0, x = slice_by_index_22)[name = tensor("reshape_111")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([-1])]; + tensor reshape_112 = reshape(shape = reshape_112_shape_0, x = var_5085)[name = tensor("reshape_112")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([-1])]; + tensor reshape_113 = reshape(shape = reshape_113_shape_0, x = cache11)[name = tensor("reshape_113")]; + tensor scatter_22_mode_0 = const()[name = tensor("scatter_22_mode_0"), val = tensor("update")]; + tensor scatter_22_axis_0 = const()[name = tensor("scatter_22_axis_0"), val = tensor(0)]; + tensor scatter_22_validate_indices_0 = const()[name = tensor("scatter_22_validate_indices_0"), val = tensor(false)]; + tensor scatter_22 = scatter(axis = scatter_22_axis_0, data = reshape_113, indices = reshape_111, mode = scatter_22_mode_0, updates = reshape_112, validate_indices = scatter_22_validate_indices_0)[name = tensor("scatter_22")]; + tensor reshape_114 = reshape(shape = shape_70, x = scatter_22)[name = tensor("reshape_114")]; + tensor var_5093_begin_0 = const()[name = tensor("op_5093_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_5093_end_0 = const()[name = tensor("op_5093_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_5093_end_mask_0 = const()[name = tensor("op_5093_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5093_squeeze_mask_0 = const()[name = tensor("op_5093_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5093 = slice_by_index(begin = var_5093_begin_0, end = var_5093_end_0, end_mask = var_5093_end_mask_0, squeeze_mask = var_5093_squeeze_mask_0, x = reshape_114)[name = tensor("op_5093")]; + tensor var_5095_axis_0 = const()[name = tensor("op_5095_axis_0"), val = tensor(1)]; + tensor var_5095_mode_0 = const()[name = tensor("op_5095_mode_0"), val = tensor("update")]; + tensor var_5095_validate_indices_0 = const()[name = tensor("op_5095_validate_indices_0"), val = tensor(false)]; + tensor var_5095 = scatter_along_axis(axis = var_5095_axis_0, data = var_5093, indices = write_indices_23, mode = var_5095_mode_0, updates = v_23, validate_indices = var_5095_validate_indices_0)[name = tensor("op_5095")]; + tensor concat_81 = const()[name = tensor("concat_81"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_82 = const()[name = tensor("concat_82"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_23_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_23_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_23_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_23_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_71 = const()[name = tensor("shape_71"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_23 = const()[name = tensor("reduce_prod_23"), val = tensor(1048576)]; + tensor range_1d_23_start_0 = const()[name = tensor("range_1d_23_start_0"), val = tensor(0)]; + tensor range_1d_23_step_0 = const()[name = tensor("range_1d_23_step_0"), val = tensor(1)]; + tensor range_1d_23 = range_1d(end = reduce_prod_23, start = range_1d_23_start_0, step = range_1d_23_step_0)[name = tensor("range_1d_23")]; + tensor reshape_115 = reshape(shape = shape_71, x = range_1d_23)[name = tensor("reshape_115")]; + tensor slice_by_index_23 = slice_by_index(begin = concat_81, begin_mask = new_cache_23_internal_tensor_assign_2_begin_mask_0, end = concat_82, end_mask = new_cache_23_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_23_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_23_internal_tensor_assign_2_stride_0, x = reshape_115)[name = tensor("slice_by_index_23")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([-1])]; + tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = slice_by_index_23)[name = tensor("reshape_116")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([-1])]; + tensor reshape_117 = reshape(shape = reshape_117_shape_0, x = var_5095)[name = tensor("reshape_117")]; + tensor reshape_118_shape_0 = const()[name = tensor("reshape_118_shape_0"), val = tensor([-1])]; + tensor reshape_118 = reshape(shape = reshape_118_shape_0, x = reshape_114)[name = tensor("reshape_118")]; + tensor scatter_23_mode_0 = const()[name = tensor("scatter_23_mode_0"), val = tensor("update")]; + tensor scatter_23_axis_0 = const()[name = tensor("scatter_23_axis_0"), val = tensor(0)]; + tensor scatter_23_validate_indices_0 = const()[name = tensor("scatter_23_validate_indices_0"), val = tensor(false)]; + tensor scatter_23 = scatter(axis = scatter_23_axis_0, data = reshape_118, indices = reshape_116, mode = scatter_23_mode_0, updates = reshape_117, validate_indices = scatter_23_validate_indices_0)[name = tensor("scatter_23")]; + tensor new_cache_23_internal_tensor_assign_2 = reshape(shape = shape_71, x = scatter_23)[name = tensor("reshape_119")]; + tensor keys_67_begin_0 = const()[name = tensor("keys_67_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_67_end_0 = const()[name = tensor("keys_67_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_67_end_mask_0 = const()[name = tensor("keys_67_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_67_squeeze_mask_0 = const()[name = tensor("keys_67_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_67 = slice_by_index(begin = keys_67_begin_0, end = keys_67_end_0, end_mask = keys_67_end_mask_0, squeeze_mask = keys_67_squeeze_mask_0, x = new_cache_23_internal_tensor_assign_2)[name = tensor("keys_67")]; + tensor values_67_begin_0 = const()[name = tensor("values_67_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_67_end_0 = const()[name = tensor("values_67_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_67_end_mask_0 = const()[name = tensor("values_67_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_67_squeeze_mask_0 = const()[name = tensor("values_67_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_67 = slice_by_index(begin = values_67_begin_0, end = values_67_end_0, end_mask = values_67_end_mask_0, squeeze_mask = values_67_squeeze_mask_0, x = new_cache_23_internal_tensor_assign_2)[name = tensor("values_67")]; + tensor var_5107 = not_equal(x = keys_67, y = keys_67)[name = tensor("op_5107")]; + tensor keys_69 = select(a = var_504, b = keys_67, cond = var_5107)[name = tensor("keys_69")]; + tensor var_5115 = not_equal(x = values_67, y = values_67)[name = tensor("op_5115")]; + tensor values_69 = select(a = var_504, b = values_67, cond = var_5115)[name = tensor("values_69")]; + tensor var_5139 = const()[name = tensor("op_5139"), val = tensor([0, 2, 1, 3])]; + tensor var_5152 = const()[name = tensor("op_5152"), val = tensor([1, 1, 1])]; + tensor var_5153 = reshape(shape = var_5152, x = position11)[name = tensor("op_5153")]; + tensor var_5170 = const()[name = tensor("op_5170"), val = tensor(0x1p+0)]; + tensor valid_len_23 = add(x = var_5153, y = var_5170)[name = tensor("valid_len_23")]; + tensor valid_mask_23 = less(x = k_positions_1_promoted, y = valid_len_23)[name = tensor("valid_mask_23")]; + tensor causal_mask_23 = less_equal(x = k_positions_1_promoted, y = var_5153)[name = tensor("causal_mask_23")]; + tensor attn_mask_45 = logical_and(x = valid_mask_23, y = causal_mask_23)[name = tensor("attn_mask_45")]; + tensor attn_mask_47_axes_0 = const()[name = tensor("attn_mask_47_axes_0"), val = tensor([1])]; + tensor attn_mask_47 = expand_dims(axes = attn_mask_47_axes_0, x = attn_mask_45)[name = tensor("attn_mask_47")]; + tensor var_5182 = const()[name = tensor("op_5182"), val = tensor([0x1.fffe5cp-4])]; + tensor var_5188_transpose_x_0 = const()[name = tensor("op_5188_transpose_x_0"), val = tensor(false)]; + tensor var_5188_transpose_y_0 = const()[name = tensor("op_5188_transpose_y_0"), val = tensor(false)]; + tensor transpose_94_perm_0 = const()[name = tensor("transpose_94_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_95_perm_0 = const()[name = tensor("transpose_95_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = keys_69)[name = tensor("transpose_169")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = q_69)[name = tensor("transpose_170")]; + tensor var_5188 = matmul(transpose_x = var_5188_transpose_x_0, transpose_y = var_5188_transpose_y_0, x = transpose_94, y = transpose_95)[name = tensor("op_5188")]; + tensor attn_weights_67 = mul(x = var_5188, y = var_5182)[name = tensor("attn_weights_67")]; + tensor var_5190 = logical_not(x = attn_mask_47)[name = tensor("op_5190")]; + tensor var_5191 = const()[name = tensor("op_5191"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_69 = select(a = var_5191, b = attn_weights_67, cond = var_5190)[name = tensor("attn_weights_69")]; + tensor var_5193 = const()[name = tensor("op_5193"), val = tensor(-1)]; + tensor attn_weights_71 = softmax(axis = var_5193, x = attn_weights_69)[name = tensor("attn_weights_71")]; + tensor attn_output_23_transpose_x_0 = const()[name = tensor("attn_output_23_transpose_x_0"), val = tensor(false)]; + tensor attn_output_23_transpose_y_0 = const()[name = tensor("attn_output_23_transpose_y_0"), val = tensor(false)]; + tensor values_71 = transpose(perm = var_5139, x = values_69)[name = tensor("transpose_171")]; + tensor attn_output_23 = matmul(transpose_x = attn_output_23_transpose_x_0, transpose_y = attn_output_23_transpose_y_0, x = attn_weights_71, y = values_71)[name = tensor("attn_output_23")]; + tensor var_5201 = const()[name = tensor("op_5201"), val = tensor([0, 2, 1, 3])]; + tensor var_5204 = const()[name = tensor("op_5204"), val = tensor([1, 1, 1024])]; + tensor var_5202 = transpose(perm = var_5201, x = attn_output_23)[name = tensor("transpose_168")]; + tensor input_115 = reshape(shape = var_5204, x = var_5202)[name = tensor("input_115")]; + tensor attn_out_23 = linear(bias = linear_0_bias_0, weight = attn11_out_proj_weight, x = input_115)[name = tensor("linear_46")]; + tensor var_5210 = const()[name = tensor("op_5210"), val = tensor(0x1p+0)]; + tensor var_5211 = add(x = position11, y = var_5210)[name = tensor("op_5211")]; + tensor input_117 = add(x = input_113, y = attn_out_23)[name = tensor("input_117")]; + tensor var_5215 = const()[name = tensor("op_5215"), val = tensor(0x1.4f8b58p-17)]; + tensor input_119_axes_0 = const()[name = tensor("input_119_axes_0"), val = tensor([-1])]; + tensor input_119 = layer_norm(axes = input_119_axes_0, beta = norm11_2_bias, epsilon = var_5215, gamma = norm11_2_weight, x = input_117)[name = tensor("input_119")]; + tensor var_5223 = linear(bias = linear_3_bias_0, weight = linear11_1_weight, x = input_119)[name = tensor("linear_47")]; + tensor input_121_mode_0 = const()[name = tensor("input_121_mode_0"), val = tensor("EXACT")]; + tensor input_121 = gelu(mode = input_121_mode_0, x = var_5223)[name = tensor("input_121")]; + tensor ffn_out_23 = linear(bias = linear_0_bias_0, weight = linear11_2_weight, x = input_121)[name = tensor("linear_48")]; + tensor input_123 = add(x = input_117, y = ffn_out_23)[name = tensor("input_123")]; + tensor var_5232 = const()[name = tensor("op_5232"), val = tensor(0x1.4f8b58p-17)]; + tensor x_25_axes_0 = const()[name = tensor("x_25_axes_0"), val = tensor([-1])]; + tensor x_25 = layer_norm(axes = x_25_axes_0, beta = norm12_1_bias, epsilon = var_5232, gamma = norm12_1_weight, x = input_123)[name = tensor("x_25")]; + tensor var_5264 = linear(bias = linear_1_bias_0, weight = attn12_in_proj_weight, x = x_25)[name = tensor("linear_49")]; + tensor var_5268 = const()[name = tensor("op_5268"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_25 = reshape(shape = var_5268, x = var_5264)[name = tensor("qkv_25")]; + tensor q_73_begin_0 = const()[name = tensor("q_73_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_73_end_0 = const()[name = tensor("q_73_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_73_end_mask_0 = const()[name = tensor("q_73_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_73_squeeze_mask_0 = const()[name = tensor("q_73_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_73 = slice_by_index(begin = q_73_begin_0, end = q_73_end_0, end_mask = q_73_end_mask_0, squeeze_mask = q_73_squeeze_mask_0, x = qkv_25)[name = tensor("q_73")]; + tensor k_49_begin_0 = const()[name = tensor("k_49_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_49_end_0 = const()[name = tensor("k_49_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_49_end_mask_0 = const()[name = tensor("k_49_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_49_squeeze_mask_0 = const()[name = tensor("k_49_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_49 = slice_by_index(begin = k_49_begin_0, end = k_49_end_0, end_mask = k_49_end_mask_0, squeeze_mask = k_49_squeeze_mask_0, x = qkv_25)[name = tensor("k_49")]; + tensor v_25_begin_0 = const()[name = tensor("v_25_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_25_end_0 = const()[name = tensor("v_25_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_25_end_mask_0 = const()[name = tensor("v_25_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_25_squeeze_mask_0 = const()[name = tensor("v_25_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_25 = slice_by_index(begin = v_25_begin_0, end = v_25_end_0, end_mask = v_25_end_mask_0, squeeze_mask = v_25_squeeze_mask_0, x = qkv_25)[name = tensor("v_25")]; + tensor freqs_25 = const()[name = tensor("freqs_25"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210643328)))]; + tensor var_5372 = const()[name = tensor("op_5372"), val = tensor([1, 1, 1, 1])]; + tensor ts_77 = reshape(shape = var_5372, x = position12)[name = tensor("ts_77")]; + tensor var_5376 = const()[name = tensor("op_5376"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_25 = reshape(shape = var_5376, x = q_73)[name = tensor("q_complex_25")]; + tensor var_5380 = const()[name = tensor("op_5380"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_25 = reshape(shape = var_5380, x = k_49)[name = tensor("k_complex_25")]; + tensor var_5384_begin_0 = const()[name = tensor("op_5384_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5384_end_0 = const()[name = tensor("op_5384_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5384_end_mask_0 = const()[name = tensor("op_5384_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5384_squeeze_mask_0 = const()[name = tensor("op_5384_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5384 = slice_by_index(begin = var_5384_begin_0, end = var_5384_end_0, end_mask = var_5384_end_mask_0, squeeze_mask = var_5384_squeeze_mask_0, x = q_complex_25)[name = tensor("op_5384")]; + tensor var_5392_begin_0 = const()[name = tensor("op_5392_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5392_end_0 = const()[name = tensor("op_5392_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5392_end_mask_0 = const()[name = tensor("op_5392_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5392_squeeze_mask_0 = const()[name = tensor("op_5392_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5392 = slice_by_index(begin = var_5392_begin_0, end = var_5392_end_0, end_mask = var_5392_end_mask_0, squeeze_mask = var_5392_squeeze_mask_0, x = q_complex_25)[name = tensor("op_5392")]; + tensor var_5400_begin_0 = const()[name = tensor("op_5400_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5400_end_0 = const()[name = tensor("op_5400_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5400_end_mask_0 = const()[name = tensor("op_5400_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5400_squeeze_mask_0 = const()[name = tensor("op_5400_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5400 = slice_by_index(begin = var_5400_begin_0, end = var_5400_end_0, end_mask = var_5400_end_mask_0, squeeze_mask = var_5400_squeeze_mask_0, x = k_complex_25)[name = tensor("op_5400")]; + tensor var_5408_begin_0 = const()[name = tensor("op_5408_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5408_end_0 = const()[name = tensor("op_5408_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5408_end_mask_0 = const()[name = tensor("op_5408_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5408_squeeze_mask_0 = const()[name = tensor("op_5408_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5408 = slice_by_index(begin = var_5408_begin_0, end = var_5408_end_0, end_mask = var_5408_end_mask_0, squeeze_mask = var_5408_squeeze_mask_0, x = k_complex_25)[name = tensor("op_5408")]; + tensor var_5414 = mul(x = freqs_25, y = ts_77)[name = tensor("op_5414")]; + tensor rotr_25 = cos(x = var_5414)[name = tensor("rotr_25")]; + tensor roti_25 = sin(x = var_5414)[name = tensor("roti_25")]; + tensor var_5418 = mul(x = var_5384, y = rotr_25)[name = tensor("op_5418")]; + tensor var_5419 = mul(x = var_5392, y = roti_25)[name = tensor("op_5419")]; + tensor qor_49 = sub(x = var_5418, y = var_5419)[name = tensor("qor_49")]; + tensor var_5422 = mul(x = var_5384, y = roti_25)[name = tensor("op_5422")]; + tensor var_5423 = mul(x = var_5392, y = rotr_25)[name = tensor("op_5423")]; + tensor qoi_49 = add(x = var_5422, y = var_5423)[name = tensor("qoi_49")]; + tensor var_5426 = mul(x = var_5400, y = rotr_25)[name = tensor("op_5426")]; + tensor var_5427 = mul(x = var_5408, y = roti_25)[name = tensor("op_5427")]; + tensor kor_49 = sub(x = var_5426, y = var_5427)[name = tensor("kor_49")]; + tensor var_5430 = mul(x = var_5400, y = roti_25)[name = tensor("op_5430")]; + tensor var_5431 = mul(x = var_5408, y = rotr_25)[name = tensor("op_5431")]; + tensor koi_49 = add(x = var_5430, y = var_5431)[name = tensor("koi_49")]; + tensor qo_25_axis_0 = const()[name = tensor("qo_25_axis_0"), val = tensor(-1)]; + tensor qo_25 = stack(axis = qo_25_axis_0, values = (qor_49, qoi_49))[name = tensor("qo_25")]; + tensor ko_25_axis_0 = const()[name = tensor("ko_25_axis_0"), val = tensor(-1)]; + tensor ko_25 = stack(axis = ko_25_axis_0, values = (kor_49, koi_49))[name = tensor("ko_25")]; + tensor var_5460 = const()[name = tensor("op_5460"), val = tensor([1, 1, 16, 64])]; + tensor q_75 = reshape(shape = var_5460, x = qo_25)[name = tensor("q_75")]; + tensor var_5462 = const()[name = tensor("op_5462"), val = tensor([1, 1, 16, 64])]; + tensor k_51 = reshape(shape = var_5462, x = ko_25)[name = tensor("k_51")]; + tensor _inversed_5484_y_0 = const()[name = tensor("_inversed_5484_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_5484 = mul(x = ts_77, y = _inversed_5484_y_0)[name = tensor("_inversed_5484")]; + tensor var_5485 = floor(x = _inversed_5484)[name = tensor("op_5485")]; + tensor var_5486 = const()[name = tensor("op_5486"), val = tensor(0x1p+9)]; + tensor var_5487 = mul(x = var_5485, y = var_5486)[name = tensor("op_5487")]; + tensor write_indices_float_51 = sub(x = ts_77, y = var_5487)[name = tensor("write_indices_float_51")]; + tensor var_5494_dtype_0 = const()[name = tensor("op_5494_dtype_0"), val = tensor("int32")]; + tensor write_indices_25_reps_0 = const()[name = tensor("write_indices_25_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_5494 = cast(dtype = var_5494_dtype_0, x = write_indices_float_51)[name = tensor("cast_443")]; + tensor write_indices_25 = tile(reps = write_indices_25_reps_0, x = var_5494)[name = tensor("write_indices_25")]; + tensor var_5502_begin_0 = const()[name = tensor("op_5502_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5502_end_0 = const()[name = tensor("op_5502_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_5502_end_mask_0 = const()[name = tensor("op_5502_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5502_squeeze_mask_0 = const()[name = tensor("op_5502_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5502 = slice_by_index(begin = var_5502_begin_0, end = var_5502_end_0, end_mask = var_5502_end_mask_0, squeeze_mask = var_5502_squeeze_mask_0, x = cache12)[name = tensor("op_5502")]; + tensor var_5504_axis_0 = const()[name = tensor("op_5504_axis_0"), val = tensor(1)]; + tensor var_5504_mode_0 = const()[name = tensor("op_5504_mode_0"), val = tensor("update")]; + tensor var_5504_validate_indices_0 = const()[name = tensor("op_5504_validate_indices_0"), val = tensor(false)]; + tensor var_5504 = scatter_along_axis(axis = var_5504_axis_0, data = var_5502, indices = write_indices_25, mode = var_5504_mode_0, updates = k_51, validate_indices = var_5504_validate_indices_0)[name = tensor("op_5504")]; + tensor concat_86 = const()[name = tensor("concat_86"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_87 = const()[name = tensor("concat_87"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_25_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_25_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_72 = const()[name = tensor("shape_72"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_24 = const()[name = tensor("reduce_prod_24"), val = tensor(1048576)]; + tensor range_1d_24_start_0 = const()[name = tensor("range_1d_24_start_0"), val = tensor(0)]; + tensor range_1d_24_step_0 = const()[name = tensor("range_1d_24_step_0"), val = tensor(1)]; + tensor range_1d_24 = range_1d(end = reduce_prod_24, start = range_1d_24_start_0, step = range_1d_24_step_0)[name = tensor("range_1d_24")]; + tensor reshape_120 = reshape(shape = shape_72, x = range_1d_24)[name = tensor("reshape_120")]; + tensor slice_by_index_24 = slice_by_index(begin = concat_86, begin_mask = new_cache_25_internal_tensor_assign_1_begin_mask_0, end = concat_87, end_mask = new_cache_25_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_25_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_25_internal_tensor_assign_1_stride_0, x = reshape_120)[name = tensor("slice_by_index_24")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([-1])]; + tensor reshape_121 = reshape(shape = reshape_121_shape_0, x = slice_by_index_24)[name = tensor("reshape_121")]; + tensor reshape_122_shape_0 = const()[name = tensor("reshape_122_shape_0"), val = tensor([-1])]; + tensor reshape_122 = reshape(shape = reshape_122_shape_0, x = var_5504)[name = tensor("reshape_122")]; + tensor reshape_123_shape_0 = const()[name = tensor("reshape_123_shape_0"), val = tensor([-1])]; + tensor reshape_123 = reshape(shape = reshape_123_shape_0, x = cache12)[name = tensor("reshape_123")]; + tensor scatter_24_mode_0 = const()[name = tensor("scatter_24_mode_0"), val = tensor("update")]; + tensor scatter_24_axis_0 = const()[name = tensor("scatter_24_axis_0"), val = tensor(0)]; + tensor scatter_24_validate_indices_0 = const()[name = tensor("scatter_24_validate_indices_0"), val = tensor(false)]; + tensor scatter_24 = scatter(axis = scatter_24_axis_0, data = reshape_123, indices = reshape_121, mode = scatter_24_mode_0, updates = reshape_122, validate_indices = scatter_24_validate_indices_0)[name = tensor("scatter_24")]; + tensor reshape_124 = reshape(shape = shape_72, x = scatter_24)[name = tensor("reshape_124")]; + tensor var_5512_begin_0 = const()[name = tensor("op_5512_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_5512_end_0 = const()[name = tensor("op_5512_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_5512_end_mask_0 = const()[name = tensor("op_5512_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5512_squeeze_mask_0 = const()[name = tensor("op_5512_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5512 = slice_by_index(begin = var_5512_begin_0, end = var_5512_end_0, end_mask = var_5512_end_mask_0, squeeze_mask = var_5512_squeeze_mask_0, x = reshape_124)[name = tensor("op_5512")]; + tensor var_5514_axis_0 = const()[name = tensor("op_5514_axis_0"), val = tensor(1)]; + tensor var_5514_mode_0 = const()[name = tensor("op_5514_mode_0"), val = tensor("update")]; + tensor var_5514_validate_indices_0 = const()[name = tensor("op_5514_validate_indices_0"), val = tensor(false)]; + tensor var_5514 = scatter_along_axis(axis = var_5514_axis_0, data = var_5512, indices = write_indices_25, mode = var_5514_mode_0, updates = v_25, validate_indices = var_5514_validate_indices_0)[name = tensor("op_5514")]; + tensor concat_88 = const()[name = tensor("concat_88"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_89 = const()[name = tensor("concat_89"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_25_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_25_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_25_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_25_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_73 = const()[name = tensor("shape_73"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_25 = const()[name = tensor("reduce_prod_25"), val = tensor(1048576)]; + tensor range_1d_25_start_0 = const()[name = tensor("range_1d_25_start_0"), val = tensor(0)]; + tensor range_1d_25_step_0 = const()[name = tensor("range_1d_25_step_0"), val = tensor(1)]; + tensor range_1d_25 = range_1d(end = reduce_prod_25, start = range_1d_25_start_0, step = range_1d_25_step_0)[name = tensor("range_1d_25")]; + tensor reshape_125 = reshape(shape = shape_73, x = range_1d_25)[name = tensor("reshape_125")]; + tensor slice_by_index_25 = slice_by_index(begin = concat_88, begin_mask = new_cache_25_internal_tensor_assign_2_begin_mask_0, end = concat_89, end_mask = new_cache_25_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_25_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_25_internal_tensor_assign_2_stride_0, x = reshape_125)[name = tensor("slice_by_index_25")]; + tensor reshape_126_shape_0 = const()[name = tensor("reshape_126_shape_0"), val = tensor([-1])]; + tensor reshape_126 = reshape(shape = reshape_126_shape_0, x = slice_by_index_25)[name = tensor("reshape_126")]; + tensor reshape_127_shape_0 = const()[name = tensor("reshape_127_shape_0"), val = tensor([-1])]; + tensor reshape_127 = reshape(shape = reshape_127_shape_0, x = var_5514)[name = tensor("reshape_127")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([-1])]; + tensor reshape_128 = reshape(shape = reshape_128_shape_0, x = reshape_124)[name = tensor("reshape_128")]; + tensor scatter_25_mode_0 = const()[name = tensor("scatter_25_mode_0"), val = tensor("update")]; + tensor scatter_25_axis_0 = const()[name = tensor("scatter_25_axis_0"), val = tensor(0)]; + tensor scatter_25_validate_indices_0 = const()[name = tensor("scatter_25_validate_indices_0"), val = tensor(false)]; + tensor scatter_25 = scatter(axis = scatter_25_axis_0, data = reshape_128, indices = reshape_126, mode = scatter_25_mode_0, updates = reshape_127, validate_indices = scatter_25_validate_indices_0)[name = tensor("scatter_25")]; + tensor new_cache_25_internal_tensor_assign_2 = reshape(shape = shape_73, x = scatter_25)[name = tensor("reshape_129")]; + tensor keys_73_begin_0 = const()[name = tensor("keys_73_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_73_end_0 = const()[name = tensor("keys_73_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_73_end_mask_0 = const()[name = tensor("keys_73_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_73_squeeze_mask_0 = const()[name = tensor("keys_73_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_73 = slice_by_index(begin = keys_73_begin_0, end = keys_73_end_0, end_mask = keys_73_end_mask_0, squeeze_mask = keys_73_squeeze_mask_0, x = new_cache_25_internal_tensor_assign_2)[name = tensor("keys_73")]; + tensor values_73_begin_0 = const()[name = tensor("values_73_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_73_end_0 = const()[name = tensor("values_73_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_73_end_mask_0 = const()[name = tensor("values_73_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_73_squeeze_mask_0 = const()[name = tensor("values_73_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_73 = slice_by_index(begin = values_73_begin_0, end = values_73_end_0, end_mask = values_73_end_mask_0, squeeze_mask = values_73_squeeze_mask_0, x = new_cache_25_internal_tensor_assign_2)[name = tensor("values_73")]; + tensor var_5526 = not_equal(x = keys_73, y = keys_73)[name = tensor("op_5526")]; + tensor keys_75 = select(a = var_504, b = keys_73, cond = var_5526)[name = tensor("keys_75")]; + tensor var_5534 = not_equal(x = values_73, y = values_73)[name = tensor("op_5534")]; + tensor values_75 = select(a = var_504, b = values_73, cond = var_5534)[name = tensor("values_75")]; + tensor var_5558 = const()[name = tensor("op_5558"), val = tensor([0, 2, 1, 3])]; + tensor var_5571 = const()[name = tensor("op_5571"), val = tensor([1, 1, 1])]; + tensor var_5572 = reshape(shape = var_5571, x = position12)[name = tensor("op_5572")]; + tensor var_5589 = const()[name = tensor("op_5589"), val = tensor(0x1p+0)]; + tensor valid_len_25 = add(x = var_5572, y = var_5589)[name = tensor("valid_len_25")]; + tensor valid_mask_25 = less(x = k_positions_1_promoted, y = valid_len_25)[name = tensor("valid_mask_25")]; + tensor causal_mask_25 = less_equal(x = k_positions_1_promoted, y = var_5572)[name = tensor("causal_mask_25")]; + tensor attn_mask_49 = logical_and(x = valid_mask_25, y = causal_mask_25)[name = tensor("attn_mask_49")]; + tensor attn_mask_51_axes_0 = const()[name = tensor("attn_mask_51_axes_0"), val = tensor([1])]; + tensor attn_mask_51 = expand_dims(axes = attn_mask_51_axes_0, x = attn_mask_49)[name = tensor("attn_mask_51")]; + tensor var_5601 = const()[name = tensor("op_5601"), val = tensor([0x1.fffe5cp-4])]; + tensor var_5607_transpose_x_0 = const()[name = tensor("op_5607_transpose_x_0"), val = tensor(false)]; + tensor var_5607_transpose_y_0 = const()[name = tensor("op_5607_transpose_y_0"), val = tensor(false)]; + tensor transpose_96_perm_0 = const()[name = tensor("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = tensor("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = keys_75)[name = tensor("transpose_165")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = q_75)[name = tensor("transpose_166")]; + tensor var_5607 = matmul(transpose_x = var_5607_transpose_x_0, transpose_y = var_5607_transpose_y_0, x = transpose_96, y = transpose_97)[name = tensor("op_5607")]; + tensor attn_weights_73 = mul(x = var_5607, y = var_5601)[name = tensor("attn_weights_73")]; + tensor var_5609 = logical_not(x = attn_mask_51)[name = tensor("op_5609")]; + tensor var_5610 = const()[name = tensor("op_5610"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_75 = select(a = var_5610, b = attn_weights_73, cond = var_5609)[name = tensor("attn_weights_75")]; + tensor var_5612 = const()[name = tensor("op_5612"), val = tensor(-1)]; + tensor attn_weights_77 = softmax(axis = var_5612, x = attn_weights_75)[name = tensor("attn_weights_77")]; + tensor attn_output_25_transpose_x_0 = const()[name = tensor("attn_output_25_transpose_x_0"), val = tensor(false)]; + tensor attn_output_25_transpose_y_0 = const()[name = tensor("attn_output_25_transpose_y_0"), val = tensor(false)]; + tensor values_77 = transpose(perm = var_5558, x = values_75)[name = tensor("transpose_167")]; + tensor attn_output_25 = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = attn_weights_77, y = values_77)[name = tensor("attn_output_25")]; + tensor var_5620 = const()[name = tensor("op_5620"), val = tensor([0, 2, 1, 3])]; + tensor var_5623 = const()[name = tensor("op_5623"), val = tensor([1, 1, 1024])]; + tensor var_5621 = transpose(perm = var_5620, x = attn_output_25)[name = tensor("transpose_164")]; + tensor input_125 = reshape(shape = var_5623, x = var_5621)[name = tensor("input_125")]; + tensor attn_out_25 = linear(bias = linear_0_bias_0, weight = attn12_out_proj_weight, x = input_125)[name = tensor("linear_50")]; + tensor var_5629 = const()[name = tensor("op_5629"), val = tensor(0x1p+0)]; + tensor var_5630 = add(x = position12, y = var_5629)[name = tensor("op_5630")]; + tensor input_127 = add(x = input_123, y = attn_out_25)[name = tensor("input_127")]; + tensor var_5634 = const()[name = tensor("op_5634"), val = tensor(0x1.4f8b58p-17)]; + tensor input_129_axes_0 = const()[name = tensor("input_129_axes_0"), val = tensor([-1])]; + tensor input_129 = layer_norm(axes = input_129_axes_0, beta = norm12_2_bias, epsilon = var_5634, gamma = norm12_2_weight, x = input_127)[name = tensor("input_129")]; + tensor var_5642 = linear(bias = linear_3_bias_0, weight = linear12_1_weight, x = input_129)[name = tensor("linear_51")]; + tensor input_131_mode_0 = const()[name = tensor("input_131_mode_0"), val = tensor("EXACT")]; + tensor input_131 = gelu(mode = input_131_mode_0, x = var_5642)[name = tensor("input_131")]; + tensor ffn_out_25 = linear(bias = linear_0_bias_0, weight = linear12_2_weight, x = input_131)[name = tensor("linear_52")]; + tensor input_133 = add(x = input_127, y = ffn_out_25)[name = tensor("input_133")]; + tensor var_5651 = const()[name = tensor("op_5651"), val = tensor(0x1.4f8b58p-17)]; + tensor x_27_axes_0 = const()[name = tensor("x_27_axes_0"), val = tensor([-1])]; + tensor x_27 = layer_norm(axes = x_27_axes_0, beta = norm13_1_bias, epsilon = var_5651, gamma = norm13_1_weight, x = input_133)[name = tensor("x_27")]; + tensor var_5683 = linear(bias = linear_1_bias_0, weight = attn13_in_proj_weight, x = x_27)[name = tensor("linear_53")]; + tensor var_5687 = const()[name = tensor("op_5687"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_27 = reshape(shape = var_5687, x = var_5683)[name = tensor("qkv_27")]; + tensor q_79_begin_0 = const()[name = tensor("q_79_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_79_end_0 = const()[name = tensor("q_79_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_79_end_mask_0 = const()[name = tensor("q_79_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_79_squeeze_mask_0 = const()[name = tensor("q_79_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_79 = slice_by_index(begin = q_79_begin_0, end = q_79_end_0, end_mask = q_79_end_mask_0, squeeze_mask = q_79_squeeze_mask_0, x = qkv_27)[name = tensor("q_79")]; + tensor k_53_begin_0 = const()[name = tensor("k_53_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_53_end_0 = const()[name = tensor("k_53_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_53_end_mask_0 = const()[name = tensor("k_53_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_53_squeeze_mask_0 = const()[name = tensor("k_53_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_53 = slice_by_index(begin = k_53_begin_0, end = k_53_end_0, end_mask = k_53_end_mask_0, squeeze_mask = k_53_squeeze_mask_0, x = qkv_27)[name = tensor("k_53")]; + tensor v_27_begin_0 = const()[name = tensor("v_27_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_27_end_0 = const()[name = tensor("v_27_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_27_end_mask_0 = const()[name = tensor("v_27_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_27_squeeze_mask_0 = const()[name = tensor("v_27_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_27 = slice_by_index(begin = v_27_begin_0, end = v_27_end_0, end_mask = v_27_end_mask_0, squeeze_mask = v_27_squeeze_mask_0, x = qkv_27)[name = tensor("v_27")]; + tensor freqs_27 = const()[name = tensor("freqs_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210643520)))]; + tensor var_5791 = const()[name = tensor("op_5791"), val = tensor([1, 1, 1, 1])]; + tensor ts_83 = reshape(shape = var_5791, x = position13)[name = tensor("ts_83")]; + tensor var_5795 = const()[name = tensor("op_5795"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_27 = reshape(shape = var_5795, x = q_79)[name = tensor("q_complex_27")]; + tensor var_5799 = const()[name = tensor("op_5799"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_27 = reshape(shape = var_5799, x = k_53)[name = tensor("k_complex_27")]; + tensor var_5803_begin_0 = const()[name = tensor("op_5803_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5803_end_0 = const()[name = tensor("op_5803_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5803_end_mask_0 = const()[name = tensor("op_5803_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5803_squeeze_mask_0 = const()[name = tensor("op_5803_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5803 = slice_by_index(begin = var_5803_begin_0, end = var_5803_end_0, end_mask = var_5803_end_mask_0, squeeze_mask = var_5803_squeeze_mask_0, x = q_complex_27)[name = tensor("op_5803")]; + tensor var_5811_begin_0 = const()[name = tensor("op_5811_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5811_end_0 = const()[name = tensor("op_5811_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5811_end_mask_0 = const()[name = tensor("op_5811_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5811_squeeze_mask_0 = const()[name = tensor("op_5811_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5811 = slice_by_index(begin = var_5811_begin_0, end = var_5811_end_0, end_mask = var_5811_end_mask_0, squeeze_mask = var_5811_squeeze_mask_0, x = q_complex_27)[name = tensor("op_5811")]; + tensor var_5819_begin_0 = const()[name = tensor("op_5819_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5819_end_0 = const()[name = tensor("op_5819_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_5819_end_mask_0 = const()[name = tensor("op_5819_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5819_squeeze_mask_0 = const()[name = tensor("op_5819_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5819 = slice_by_index(begin = var_5819_begin_0, end = var_5819_end_0, end_mask = var_5819_end_mask_0, squeeze_mask = var_5819_squeeze_mask_0, x = k_complex_27)[name = tensor("op_5819")]; + tensor var_5827_begin_0 = const()[name = tensor("op_5827_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_5827_end_0 = const()[name = tensor("op_5827_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_5827_end_mask_0 = const()[name = tensor("op_5827_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_5827_squeeze_mask_0 = const()[name = tensor("op_5827_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_5827 = slice_by_index(begin = var_5827_begin_0, end = var_5827_end_0, end_mask = var_5827_end_mask_0, squeeze_mask = var_5827_squeeze_mask_0, x = k_complex_27)[name = tensor("op_5827")]; + tensor var_5833 = mul(x = freqs_27, y = ts_83)[name = tensor("op_5833")]; + tensor rotr_27 = cos(x = var_5833)[name = tensor("rotr_27")]; + tensor roti_27 = sin(x = var_5833)[name = tensor("roti_27")]; + tensor var_5837 = mul(x = var_5803, y = rotr_27)[name = tensor("op_5837")]; + tensor var_5838 = mul(x = var_5811, y = roti_27)[name = tensor("op_5838")]; + tensor qor_53 = sub(x = var_5837, y = var_5838)[name = tensor("qor_53")]; + tensor var_5841 = mul(x = var_5803, y = roti_27)[name = tensor("op_5841")]; + tensor var_5842 = mul(x = var_5811, y = rotr_27)[name = tensor("op_5842")]; + tensor qoi_53 = add(x = var_5841, y = var_5842)[name = tensor("qoi_53")]; + tensor var_5845 = mul(x = var_5819, y = rotr_27)[name = tensor("op_5845")]; + tensor var_5846 = mul(x = var_5827, y = roti_27)[name = tensor("op_5846")]; + tensor kor_53 = sub(x = var_5845, y = var_5846)[name = tensor("kor_53")]; + tensor var_5849 = mul(x = var_5819, y = roti_27)[name = tensor("op_5849")]; + tensor var_5850 = mul(x = var_5827, y = rotr_27)[name = tensor("op_5850")]; + tensor koi_53 = add(x = var_5849, y = var_5850)[name = tensor("koi_53")]; + tensor qo_27_axis_0 = const()[name = tensor("qo_27_axis_0"), val = tensor(-1)]; + tensor qo_27 = stack(axis = qo_27_axis_0, values = (qor_53, qoi_53))[name = tensor("qo_27")]; + tensor ko_27_axis_0 = const()[name = tensor("ko_27_axis_0"), val = tensor(-1)]; + tensor ko_27 = stack(axis = ko_27_axis_0, values = (kor_53, koi_53))[name = tensor("ko_27")]; + tensor var_5879 = const()[name = tensor("op_5879"), val = tensor([1, 1, 16, 64])]; + tensor q_81 = reshape(shape = var_5879, x = qo_27)[name = tensor("q_81")]; + tensor var_5881 = const()[name = tensor("op_5881"), val = tensor([1, 1, 16, 64])]; + tensor k_55 = reshape(shape = var_5881, x = ko_27)[name = tensor("k_55")]; + tensor _inversed_5903_y_0 = const()[name = tensor("_inversed_5903_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_5903 = mul(x = ts_83, y = _inversed_5903_y_0)[name = tensor("_inversed_5903")]; + tensor var_5904 = floor(x = _inversed_5903)[name = tensor("op_5904")]; + tensor var_5905 = const()[name = tensor("op_5905"), val = tensor(0x1p+9)]; + tensor var_5906 = mul(x = var_5904, y = var_5905)[name = tensor("op_5906")]; + tensor write_indices_float_55 = sub(x = ts_83, y = var_5906)[name = tensor("write_indices_float_55")]; + tensor var_5913_dtype_0 = const()[name = tensor("op_5913_dtype_0"), val = tensor("int32")]; + tensor write_indices_27_reps_0 = const()[name = tensor("write_indices_27_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_5913 = cast(dtype = var_5913_dtype_0, x = write_indices_float_55)[name = tensor("cast_442")]; + tensor write_indices_27 = tile(reps = write_indices_27_reps_0, x = var_5913)[name = tensor("write_indices_27")]; + tensor var_5921_begin_0 = const()[name = tensor("op_5921_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_5921_end_0 = const()[name = tensor("op_5921_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_5921_end_mask_0 = const()[name = tensor("op_5921_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5921_squeeze_mask_0 = const()[name = tensor("op_5921_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5921 = slice_by_index(begin = var_5921_begin_0, end = var_5921_end_0, end_mask = var_5921_end_mask_0, squeeze_mask = var_5921_squeeze_mask_0, x = cache13)[name = tensor("op_5921")]; + tensor var_5923_axis_0 = const()[name = tensor("op_5923_axis_0"), val = tensor(1)]; + tensor var_5923_mode_0 = const()[name = tensor("op_5923_mode_0"), val = tensor("update")]; + tensor var_5923_validate_indices_0 = const()[name = tensor("op_5923_validate_indices_0"), val = tensor(false)]; + tensor var_5923 = scatter_along_axis(axis = var_5923_axis_0, data = var_5921, indices = write_indices_27, mode = var_5923_mode_0, updates = k_55, validate_indices = var_5923_validate_indices_0)[name = tensor("op_5923")]; + tensor concat_93 = const()[name = tensor("concat_93"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_94 = const()[name = tensor("concat_94"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_27_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_27_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_74 = const()[name = tensor("shape_74"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_26 = const()[name = tensor("reduce_prod_26"), val = tensor(1048576)]; + tensor range_1d_26_start_0 = const()[name = tensor("range_1d_26_start_0"), val = tensor(0)]; + tensor range_1d_26_step_0 = const()[name = tensor("range_1d_26_step_0"), val = tensor(1)]; + tensor range_1d_26 = range_1d(end = reduce_prod_26, start = range_1d_26_start_0, step = range_1d_26_step_0)[name = tensor("range_1d_26")]; + tensor reshape_130 = reshape(shape = shape_74, x = range_1d_26)[name = tensor("reshape_130")]; + tensor slice_by_index_26 = slice_by_index(begin = concat_93, begin_mask = new_cache_27_internal_tensor_assign_1_begin_mask_0, end = concat_94, end_mask = new_cache_27_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_27_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_27_internal_tensor_assign_1_stride_0, x = reshape_130)[name = tensor("slice_by_index_26")]; + tensor reshape_131_shape_0 = const()[name = tensor("reshape_131_shape_0"), val = tensor([-1])]; + tensor reshape_131 = reshape(shape = reshape_131_shape_0, x = slice_by_index_26)[name = tensor("reshape_131")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([-1])]; + tensor reshape_132 = reshape(shape = reshape_132_shape_0, x = var_5923)[name = tensor("reshape_132")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([-1])]; + tensor reshape_133 = reshape(shape = reshape_133_shape_0, x = cache13)[name = tensor("reshape_133")]; + tensor scatter_26_mode_0 = const()[name = tensor("scatter_26_mode_0"), val = tensor("update")]; + tensor scatter_26_axis_0 = const()[name = tensor("scatter_26_axis_0"), val = tensor(0)]; + tensor scatter_26_validate_indices_0 = const()[name = tensor("scatter_26_validate_indices_0"), val = tensor(false)]; + tensor scatter_26 = scatter(axis = scatter_26_axis_0, data = reshape_133, indices = reshape_131, mode = scatter_26_mode_0, updates = reshape_132, validate_indices = scatter_26_validate_indices_0)[name = tensor("scatter_26")]; + tensor reshape_134 = reshape(shape = shape_74, x = scatter_26)[name = tensor("reshape_134")]; + tensor var_5931_begin_0 = const()[name = tensor("op_5931_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_5931_end_0 = const()[name = tensor("op_5931_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_5931_end_mask_0 = const()[name = tensor("op_5931_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_5931_squeeze_mask_0 = const()[name = tensor("op_5931_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_5931 = slice_by_index(begin = var_5931_begin_0, end = var_5931_end_0, end_mask = var_5931_end_mask_0, squeeze_mask = var_5931_squeeze_mask_0, x = reshape_134)[name = tensor("op_5931")]; + tensor var_5933_axis_0 = const()[name = tensor("op_5933_axis_0"), val = tensor(1)]; + tensor var_5933_mode_0 = const()[name = tensor("op_5933_mode_0"), val = tensor("update")]; + tensor var_5933_validate_indices_0 = const()[name = tensor("op_5933_validate_indices_0"), val = tensor(false)]; + tensor var_5933 = scatter_along_axis(axis = var_5933_axis_0, data = var_5931, indices = write_indices_27, mode = var_5933_mode_0, updates = v_27, validate_indices = var_5933_validate_indices_0)[name = tensor("op_5933")]; + tensor concat_95 = const()[name = tensor("concat_95"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_96 = const()[name = tensor("concat_96"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_27_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_27_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_27_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_27_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_75 = const()[name = tensor("shape_75"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_27 = const()[name = tensor("reduce_prod_27"), val = tensor(1048576)]; + tensor range_1d_27_start_0 = const()[name = tensor("range_1d_27_start_0"), val = tensor(0)]; + tensor range_1d_27_step_0 = const()[name = tensor("range_1d_27_step_0"), val = tensor(1)]; + tensor range_1d_27 = range_1d(end = reduce_prod_27, start = range_1d_27_start_0, step = range_1d_27_step_0)[name = tensor("range_1d_27")]; + tensor reshape_135 = reshape(shape = shape_75, x = range_1d_27)[name = tensor("reshape_135")]; + tensor slice_by_index_27 = slice_by_index(begin = concat_95, begin_mask = new_cache_27_internal_tensor_assign_2_begin_mask_0, end = concat_96, end_mask = new_cache_27_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_27_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_27_internal_tensor_assign_2_stride_0, x = reshape_135)[name = tensor("slice_by_index_27")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([-1])]; + tensor reshape_136 = reshape(shape = reshape_136_shape_0, x = slice_by_index_27)[name = tensor("reshape_136")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([-1])]; + tensor reshape_137 = reshape(shape = reshape_137_shape_0, x = var_5933)[name = tensor("reshape_137")]; + tensor reshape_138_shape_0 = const()[name = tensor("reshape_138_shape_0"), val = tensor([-1])]; + tensor reshape_138 = reshape(shape = reshape_138_shape_0, x = reshape_134)[name = tensor("reshape_138")]; + tensor scatter_27_mode_0 = const()[name = tensor("scatter_27_mode_0"), val = tensor("update")]; + tensor scatter_27_axis_0 = const()[name = tensor("scatter_27_axis_0"), val = tensor(0)]; + tensor scatter_27_validate_indices_0 = const()[name = tensor("scatter_27_validate_indices_0"), val = tensor(false)]; + tensor scatter_27 = scatter(axis = scatter_27_axis_0, data = reshape_138, indices = reshape_136, mode = scatter_27_mode_0, updates = reshape_137, validate_indices = scatter_27_validate_indices_0)[name = tensor("scatter_27")]; + tensor new_cache_27_internal_tensor_assign_2 = reshape(shape = shape_75, x = scatter_27)[name = tensor("reshape_139")]; + tensor keys_79_begin_0 = const()[name = tensor("keys_79_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_79_end_0 = const()[name = tensor("keys_79_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_79_end_mask_0 = const()[name = tensor("keys_79_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_79_squeeze_mask_0 = const()[name = tensor("keys_79_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_79 = slice_by_index(begin = keys_79_begin_0, end = keys_79_end_0, end_mask = keys_79_end_mask_0, squeeze_mask = keys_79_squeeze_mask_0, x = new_cache_27_internal_tensor_assign_2)[name = tensor("keys_79")]; + tensor values_79_begin_0 = const()[name = tensor("values_79_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_79_end_0 = const()[name = tensor("values_79_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_79_end_mask_0 = const()[name = tensor("values_79_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_79_squeeze_mask_0 = const()[name = tensor("values_79_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_79 = slice_by_index(begin = values_79_begin_0, end = values_79_end_0, end_mask = values_79_end_mask_0, squeeze_mask = values_79_squeeze_mask_0, x = new_cache_27_internal_tensor_assign_2)[name = tensor("values_79")]; + tensor var_5945 = not_equal(x = keys_79, y = keys_79)[name = tensor("op_5945")]; + tensor keys_81 = select(a = var_504, b = keys_79, cond = var_5945)[name = tensor("keys_81")]; + tensor var_5953 = not_equal(x = values_79, y = values_79)[name = tensor("op_5953")]; + tensor values_81 = select(a = var_504, b = values_79, cond = var_5953)[name = tensor("values_81")]; + tensor var_5977 = const()[name = tensor("op_5977"), val = tensor([0, 2, 1, 3])]; + tensor var_5990 = const()[name = tensor("op_5990"), val = tensor([1, 1, 1])]; + tensor var_5991 = reshape(shape = var_5990, x = position13)[name = tensor("op_5991")]; + tensor var_6008 = const()[name = tensor("op_6008"), val = tensor(0x1p+0)]; + tensor valid_len_27 = add(x = var_5991, y = var_6008)[name = tensor("valid_len_27")]; + tensor valid_mask_27 = less(x = k_positions_1_promoted, y = valid_len_27)[name = tensor("valid_mask_27")]; + tensor causal_mask_27 = less_equal(x = k_positions_1_promoted, y = var_5991)[name = tensor("causal_mask_27")]; + tensor attn_mask_53 = logical_and(x = valid_mask_27, y = causal_mask_27)[name = tensor("attn_mask_53")]; + tensor attn_mask_55_axes_0 = const()[name = tensor("attn_mask_55_axes_0"), val = tensor([1])]; + tensor attn_mask_55 = expand_dims(axes = attn_mask_55_axes_0, x = attn_mask_53)[name = tensor("attn_mask_55")]; + tensor var_6020 = const()[name = tensor("op_6020"), val = tensor([0x1.fffe5cp-4])]; + tensor var_6026_transpose_x_0 = const()[name = tensor("op_6026_transpose_x_0"), val = tensor(false)]; + tensor var_6026_transpose_y_0 = const()[name = tensor("op_6026_transpose_y_0"), val = tensor(false)]; + tensor transpose_98_perm_0 = const()[name = tensor("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = tensor("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = keys_81)[name = tensor("transpose_161")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = q_81)[name = tensor("transpose_162")]; + tensor var_6026 = matmul(transpose_x = var_6026_transpose_x_0, transpose_y = var_6026_transpose_y_0, x = transpose_98, y = transpose_99)[name = tensor("op_6026")]; + tensor attn_weights_79 = mul(x = var_6026, y = var_6020)[name = tensor("attn_weights_79")]; + tensor var_6028 = logical_not(x = attn_mask_55)[name = tensor("op_6028")]; + tensor var_6029 = const()[name = tensor("op_6029"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_81 = select(a = var_6029, b = attn_weights_79, cond = var_6028)[name = tensor("attn_weights_81")]; + tensor var_6031 = const()[name = tensor("op_6031"), val = tensor(-1)]; + tensor attn_weights_83 = softmax(axis = var_6031, x = attn_weights_81)[name = tensor("attn_weights_83")]; + tensor attn_output_27_transpose_x_0 = const()[name = tensor("attn_output_27_transpose_x_0"), val = tensor(false)]; + tensor attn_output_27_transpose_y_0 = const()[name = tensor("attn_output_27_transpose_y_0"), val = tensor(false)]; + tensor values_83 = transpose(perm = var_5977, x = values_81)[name = tensor("transpose_163")]; + tensor attn_output_27 = matmul(transpose_x = attn_output_27_transpose_x_0, transpose_y = attn_output_27_transpose_y_0, x = attn_weights_83, y = values_83)[name = tensor("attn_output_27")]; + tensor var_6039 = const()[name = tensor("op_6039"), val = tensor([0, 2, 1, 3])]; + tensor var_6042 = const()[name = tensor("op_6042"), val = tensor([1, 1, 1024])]; + tensor var_6040 = transpose(perm = var_6039, x = attn_output_27)[name = tensor("transpose_160")]; + tensor input_135 = reshape(shape = var_6042, x = var_6040)[name = tensor("input_135")]; + tensor attn_out_27 = linear(bias = linear_0_bias_0, weight = attn13_out_proj_weight, x = input_135)[name = tensor("linear_54")]; + tensor var_6048 = const()[name = tensor("op_6048"), val = tensor(0x1p+0)]; + tensor var_6049 = add(x = position13, y = var_6048)[name = tensor("op_6049")]; + tensor input_137 = add(x = input_133, y = attn_out_27)[name = tensor("input_137")]; + tensor var_6053 = const()[name = tensor("op_6053"), val = tensor(0x1.4f8b58p-17)]; + tensor input_139_axes_0 = const()[name = tensor("input_139_axes_0"), val = tensor([-1])]; + tensor input_139 = layer_norm(axes = input_139_axes_0, beta = norm13_2_bias, epsilon = var_6053, gamma = norm13_2_weight, x = input_137)[name = tensor("input_139")]; + tensor var_6061 = linear(bias = linear_3_bias_0, weight = linear13_1_weight, x = input_139)[name = tensor("linear_55")]; + tensor input_141_mode_0 = const()[name = tensor("input_141_mode_0"), val = tensor("EXACT")]; + tensor input_141 = gelu(mode = input_141_mode_0, x = var_6061)[name = tensor("input_141")]; + tensor ffn_out_27 = linear(bias = linear_0_bias_0, weight = linear13_2_weight, x = input_141)[name = tensor("linear_56")]; + tensor input_143 = add(x = input_137, y = ffn_out_27)[name = tensor("input_143")]; + tensor var_6070 = const()[name = tensor("op_6070"), val = tensor(0x1.4f8b58p-17)]; + tensor x_29_axes_0 = const()[name = tensor("x_29_axes_0"), val = tensor([-1])]; + tensor x_29 = layer_norm(axes = x_29_axes_0, beta = norm14_1_bias, epsilon = var_6070, gamma = norm14_1_weight, x = input_143)[name = tensor("x_29")]; + tensor var_6102 = linear(bias = linear_1_bias_0, weight = attn14_in_proj_weight, x = x_29)[name = tensor("linear_57")]; + tensor var_6106 = const()[name = tensor("op_6106"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_29 = reshape(shape = var_6106, x = var_6102)[name = tensor("qkv_29")]; + tensor q_85_begin_0 = const()[name = tensor("q_85_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_85_end_0 = const()[name = tensor("q_85_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_85_end_mask_0 = const()[name = tensor("q_85_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_85_squeeze_mask_0 = const()[name = tensor("q_85_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_85 = slice_by_index(begin = q_85_begin_0, end = q_85_end_0, end_mask = q_85_end_mask_0, squeeze_mask = q_85_squeeze_mask_0, x = qkv_29)[name = tensor("q_85")]; + tensor k_57_begin_0 = const()[name = tensor("k_57_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_57_end_0 = const()[name = tensor("k_57_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_57_end_mask_0 = const()[name = tensor("k_57_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_57_squeeze_mask_0 = const()[name = tensor("k_57_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_57 = slice_by_index(begin = k_57_begin_0, end = k_57_end_0, end_mask = k_57_end_mask_0, squeeze_mask = k_57_squeeze_mask_0, x = qkv_29)[name = tensor("k_57")]; + tensor v_29_begin_0 = const()[name = tensor("v_29_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_29_end_0 = const()[name = tensor("v_29_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_29_end_mask_0 = const()[name = tensor("v_29_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_29_squeeze_mask_0 = const()[name = tensor("v_29_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_29 = slice_by_index(begin = v_29_begin_0, end = v_29_end_0, end_mask = v_29_end_mask_0, squeeze_mask = v_29_squeeze_mask_0, x = qkv_29)[name = tensor("v_29")]; + tensor freqs_29 = const()[name = tensor("freqs_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210643712)))]; + tensor var_6210 = const()[name = tensor("op_6210"), val = tensor([1, 1, 1, 1])]; + tensor ts_89 = reshape(shape = var_6210, x = position14)[name = tensor("ts_89")]; + tensor var_6214 = const()[name = tensor("op_6214"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_29 = reshape(shape = var_6214, x = q_85)[name = tensor("q_complex_29")]; + tensor var_6218 = const()[name = tensor("op_6218"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_29 = reshape(shape = var_6218, x = k_57)[name = tensor("k_complex_29")]; + tensor var_6222_begin_0 = const()[name = tensor("op_6222_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6222_end_0 = const()[name = tensor("op_6222_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6222_end_mask_0 = const()[name = tensor("op_6222_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6222_squeeze_mask_0 = const()[name = tensor("op_6222_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6222 = slice_by_index(begin = var_6222_begin_0, end = var_6222_end_0, end_mask = var_6222_end_mask_0, squeeze_mask = var_6222_squeeze_mask_0, x = q_complex_29)[name = tensor("op_6222")]; + tensor var_6230_begin_0 = const()[name = tensor("op_6230_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6230_end_0 = const()[name = tensor("op_6230_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6230_end_mask_0 = const()[name = tensor("op_6230_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6230_squeeze_mask_0 = const()[name = tensor("op_6230_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6230 = slice_by_index(begin = var_6230_begin_0, end = var_6230_end_0, end_mask = var_6230_end_mask_0, squeeze_mask = var_6230_squeeze_mask_0, x = q_complex_29)[name = tensor("op_6230")]; + tensor var_6238_begin_0 = const()[name = tensor("op_6238_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6238_end_0 = const()[name = tensor("op_6238_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6238_end_mask_0 = const()[name = tensor("op_6238_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6238_squeeze_mask_0 = const()[name = tensor("op_6238_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6238 = slice_by_index(begin = var_6238_begin_0, end = var_6238_end_0, end_mask = var_6238_end_mask_0, squeeze_mask = var_6238_squeeze_mask_0, x = k_complex_29)[name = tensor("op_6238")]; + tensor var_6246_begin_0 = const()[name = tensor("op_6246_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6246_end_0 = const()[name = tensor("op_6246_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6246_end_mask_0 = const()[name = tensor("op_6246_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6246_squeeze_mask_0 = const()[name = tensor("op_6246_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6246 = slice_by_index(begin = var_6246_begin_0, end = var_6246_end_0, end_mask = var_6246_end_mask_0, squeeze_mask = var_6246_squeeze_mask_0, x = k_complex_29)[name = tensor("op_6246")]; + tensor var_6252 = mul(x = freqs_29, y = ts_89)[name = tensor("op_6252")]; + tensor rotr_29 = cos(x = var_6252)[name = tensor("rotr_29")]; + tensor roti_29 = sin(x = var_6252)[name = tensor("roti_29")]; + tensor var_6256 = mul(x = var_6222, y = rotr_29)[name = tensor("op_6256")]; + tensor var_6257 = mul(x = var_6230, y = roti_29)[name = tensor("op_6257")]; + tensor qor_57 = sub(x = var_6256, y = var_6257)[name = tensor("qor_57")]; + tensor var_6260 = mul(x = var_6222, y = roti_29)[name = tensor("op_6260")]; + tensor var_6261 = mul(x = var_6230, y = rotr_29)[name = tensor("op_6261")]; + tensor qoi_57 = add(x = var_6260, y = var_6261)[name = tensor("qoi_57")]; + tensor var_6264 = mul(x = var_6238, y = rotr_29)[name = tensor("op_6264")]; + tensor var_6265 = mul(x = var_6246, y = roti_29)[name = tensor("op_6265")]; + tensor kor_57 = sub(x = var_6264, y = var_6265)[name = tensor("kor_57")]; + tensor var_6268 = mul(x = var_6238, y = roti_29)[name = tensor("op_6268")]; + tensor var_6269 = mul(x = var_6246, y = rotr_29)[name = tensor("op_6269")]; + tensor koi_57 = add(x = var_6268, y = var_6269)[name = tensor("koi_57")]; + tensor qo_29_axis_0 = const()[name = tensor("qo_29_axis_0"), val = tensor(-1)]; + tensor qo_29 = stack(axis = qo_29_axis_0, values = (qor_57, qoi_57))[name = tensor("qo_29")]; + tensor ko_29_axis_0 = const()[name = tensor("ko_29_axis_0"), val = tensor(-1)]; + tensor ko_29 = stack(axis = ko_29_axis_0, values = (kor_57, koi_57))[name = tensor("ko_29")]; + tensor var_6298 = const()[name = tensor("op_6298"), val = tensor([1, 1, 16, 64])]; + tensor q_87 = reshape(shape = var_6298, x = qo_29)[name = tensor("q_87")]; + tensor var_6300 = const()[name = tensor("op_6300"), val = tensor([1, 1, 16, 64])]; + tensor k_59 = reshape(shape = var_6300, x = ko_29)[name = tensor("k_59")]; + tensor _inversed_6322_y_0 = const()[name = tensor("_inversed_6322_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_6322 = mul(x = ts_89, y = _inversed_6322_y_0)[name = tensor("_inversed_6322")]; + tensor var_6323 = floor(x = _inversed_6322)[name = tensor("op_6323")]; + tensor var_6324 = const()[name = tensor("op_6324"), val = tensor(0x1p+9)]; + tensor var_6325 = mul(x = var_6323, y = var_6324)[name = tensor("op_6325")]; + tensor write_indices_float_59 = sub(x = ts_89, y = var_6325)[name = tensor("write_indices_float_59")]; + tensor var_6332_dtype_0 = const()[name = tensor("op_6332_dtype_0"), val = tensor("int32")]; + tensor write_indices_29_reps_0 = const()[name = tensor("write_indices_29_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_6332 = cast(dtype = var_6332_dtype_0, x = write_indices_float_59)[name = tensor("cast_441")]; + tensor write_indices_29 = tile(reps = write_indices_29_reps_0, x = var_6332)[name = tensor("write_indices_29")]; + tensor var_6340_begin_0 = const()[name = tensor("op_6340_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6340_end_0 = const()[name = tensor("op_6340_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_6340_end_mask_0 = const()[name = tensor("op_6340_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6340_squeeze_mask_0 = const()[name = tensor("op_6340_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6340 = slice_by_index(begin = var_6340_begin_0, end = var_6340_end_0, end_mask = var_6340_end_mask_0, squeeze_mask = var_6340_squeeze_mask_0, x = cache14)[name = tensor("op_6340")]; + tensor var_6342_axis_0 = const()[name = tensor("op_6342_axis_0"), val = tensor(1)]; + tensor var_6342_mode_0 = const()[name = tensor("op_6342_mode_0"), val = tensor("update")]; + tensor var_6342_validate_indices_0 = const()[name = tensor("op_6342_validate_indices_0"), val = tensor(false)]; + tensor var_6342 = scatter_along_axis(axis = var_6342_axis_0, data = var_6340, indices = write_indices_29, mode = var_6342_mode_0, updates = k_59, validate_indices = var_6342_validate_indices_0)[name = tensor("op_6342")]; + tensor concat_100 = const()[name = tensor("concat_100"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_101 = const()[name = tensor("concat_101"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_29_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_29_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_76 = const()[name = tensor("shape_76"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_28 = const()[name = tensor("reduce_prod_28"), val = tensor(1048576)]; + tensor range_1d_28_start_0 = const()[name = tensor("range_1d_28_start_0"), val = tensor(0)]; + tensor range_1d_28_step_0 = const()[name = tensor("range_1d_28_step_0"), val = tensor(1)]; + tensor range_1d_28 = range_1d(end = reduce_prod_28, start = range_1d_28_start_0, step = range_1d_28_step_0)[name = tensor("range_1d_28")]; + tensor reshape_140 = reshape(shape = shape_76, x = range_1d_28)[name = tensor("reshape_140")]; + tensor slice_by_index_28 = slice_by_index(begin = concat_100, begin_mask = new_cache_29_internal_tensor_assign_1_begin_mask_0, end = concat_101, end_mask = new_cache_29_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_29_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_29_internal_tensor_assign_1_stride_0, x = reshape_140)[name = tensor("slice_by_index_28")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([-1])]; + tensor reshape_141 = reshape(shape = reshape_141_shape_0, x = slice_by_index_28)[name = tensor("reshape_141")]; + tensor reshape_142_shape_0 = const()[name = tensor("reshape_142_shape_0"), val = tensor([-1])]; + tensor reshape_142 = reshape(shape = reshape_142_shape_0, x = var_6342)[name = tensor("reshape_142")]; + tensor reshape_143_shape_0 = const()[name = tensor("reshape_143_shape_0"), val = tensor([-1])]; + tensor reshape_143 = reshape(shape = reshape_143_shape_0, x = cache14)[name = tensor("reshape_143")]; + tensor scatter_28_mode_0 = const()[name = tensor("scatter_28_mode_0"), val = tensor("update")]; + tensor scatter_28_axis_0 = const()[name = tensor("scatter_28_axis_0"), val = tensor(0)]; + tensor scatter_28_validate_indices_0 = const()[name = tensor("scatter_28_validate_indices_0"), val = tensor(false)]; + tensor scatter_28 = scatter(axis = scatter_28_axis_0, data = reshape_143, indices = reshape_141, mode = scatter_28_mode_0, updates = reshape_142, validate_indices = scatter_28_validate_indices_0)[name = tensor("scatter_28")]; + tensor reshape_144 = reshape(shape = shape_76, x = scatter_28)[name = tensor("reshape_144")]; + tensor var_6350_begin_0 = const()[name = tensor("op_6350_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_6350_end_0 = const()[name = tensor("op_6350_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_6350_end_mask_0 = const()[name = tensor("op_6350_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6350_squeeze_mask_0 = const()[name = tensor("op_6350_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6350 = slice_by_index(begin = var_6350_begin_0, end = var_6350_end_0, end_mask = var_6350_end_mask_0, squeeze_mask = var_6350_squeeze_mask_0, x = reshape_144)[name = tensor("op_6350")]; + tensor var_6352_axis_0 = const()[name = tensor("op_6352_axis_0"), val = tensor(1)]; + tensor var_6352_mode_0 = const()[name = tensor("op_6352_mode_0"), val = tensor("update")]; + tensor var_6352_validate_indices_0 = const()[name = tensor("op_6352_validate_indices_0"), val = tensor(false)]; + tensor var_6352 = scatter_along_axis(axis = var_6352_axis_0, data = var_6350, indices = write_indices_29, mode = var_6352_mode_0, updates = v_29, validate_indices = var_6352_validate_indices_0)[name = tensor("op_6352")]; + tensor concat_102 = const()[name = tensor("concat_102"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_103 = const()[name = tensor("concat_103"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_29_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_29_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_29_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_29_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_77 = const()[name = tensor("shape_77"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_29 = const()[name = tensor("reduce_prod_29"), val = tensor(1048576)]; + tensor range_1d_29_start_0 = const()[name = tensor("range_1d_29_start_0"), val = tensor(0)]; + tensor range_1d_29_step_0 = const()[name = tensor("range_1d_29_step_0"), val = tensor(1)]; + tensor range_1d_29 = range_1d(end = reduce_prod_29, start = range_1d_29_start_0, step = range_1d_29_step_0)[name = tensor("range_1d_29")]; + tensor reshape_145 = reshape(shape = shape_77, x = range_1d_29)[name = tensor("reshape_145")]; + tensor slice_by_index_29 = slice_by_index(begin = concat_102, begin_mask = new_cache_29_internal_tensor_assign_2_begin_mask_0, end = concat_103, end_mask = new_cache_29_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_29_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_29_internal_tensor_assign_2_stride_0, x = reshape_145)[name = tensor("slice_by_index_29")]; + tensor reshape_146_shape_0 = const()[name = tensor("reshape_146_shape_0"), val = tensor([-1])]; + tensor reshape_146 = reshape(shape = reshape_146_shape_0, x = slice_by_index_29)[name = tensor("reshape_146")]; + tensor reshape_147_shape_0 = const()[name = tensor("reshape_147_shape_0"), val = tensor([-1])]; + tensor reshape_147 = reshape(shape = reshape_147_shape_0, x = var_6352)[name = tensor("reshape_147")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([-1])]; + tensor reshape_148 = reshape(shape = reshape_148_shape_0, x = reshape_144)[name = tensor("reshape_148")]; + tensor scatter_29_mode_0 = const()[name = tensor("scatter_29_mode_0"), val = tensor("update")]; + tensor scatter_29_axis_0 = const()[name = tensor("scatter_29_axis_0"), val = tensor(0)]; + tensor scatter_29_validate_indices_0 = const()[name = tensor("scatter_29_validate_indices_0"), val = tensor(false)]; + tensor scatter_29 = scatter(axis = scatter_29_axis_0, data = reshape_148, indices = reshape_146, mode = scatter_29_mode_0, updates = reshape_147, validate_indices = scatter_29_validate_indices_0)[name = tensor("scatter_29")]; + tensor new_cache_29_internal_tensor_assign_2 = reshape(shape = shape_77, x = scatter_29)[name = tensor("reshape_149")]; + tensor keys_85_begin_0 = const()[name = tensor("keys_85_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_85_end_0 = const()[name = tensor("keys_85_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_85_end_mask_0 = const()[name = tensor("keys_85_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_85_squeeze_mask_0 = const()[name = tensor("keys_85_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_85 = slice_by_index(begin = keys_85_begin_0, end = keys_85_end_0, end_mask = keys_85_end_mask_0, squeeze_mask = keys_85_squeeze_mask_0, x = new_cache_29_internal_tensor_assign_2)[name = tensor("keys_85")]; + tensor values_85_begin_0 = const()[name = tensor("values_85_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_85_end_0 = const()[name = tensor("values_85_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_85_end_mask_0 = const()[name = tensor("values_85_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_85_squeeze_mask_0 = const()[name = tensor("values_85_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_85 = slice_by_index(begin = values_85_begin_0, end = values_85_end_0, end_mask = values_85_end_mask_0, squeeze_mask = values_85_squeeze_mask_0, x = new_cache_29_internal_tensor_assign_2)[name = tensor("values_85")]; + tensor var_6364 = not_equal(x = keys_85, y = keys_85)[name = tensor("op_6364")]; + tensor keys_87 = select(a = var_504, b = keys_85, cond = var_6364)[name = tensor("keys_87")]; + tensor var_6372 = not_equal(x = values_85, y = values_85)[name = tensor("op_6372")]; + tensor values_87 = select(a = var_504, b = values_85, cond = var_6372)[name = tensor("values_87")]; + tensor var_6396 = const()[name = tensor("op_6396"), val = tensor([0, 2, 1, 3])]; + tensor var_6409 = const()[name = tensor("op_6409"), val = tensor([1, 1, 1])]; + tensor var_6410 = reshape(shape = var_6409, x = position14)[name = tensor("op_6410")]; + tensor var_6427 = const()[name = tensor("op_6427"), val = tensor(0x1p+0)]; + tensor valid_len_29 = add(x = var_6410, y = var_6427)[name = tensor("valid_len_29")]; + tensor valid_mask_29 = less(x = k_positions_1_promoted, y = valid_len_29)[name = tensor("valid_mask_29")]; + tensor causal_mask_29 = less_equal(x = k_positions_1_promoted, y = var_6410)[name = tensor("causal_mask_29")]; + tensor attn_mask_57 = logical_and(x = valid_mask_29, y = causal_mask_29)[name = tensor("attn_mask_57")]; + tensor attn_mask_59_axes_0 = const()[name = tensor("attn_mask_59_axes_0"), val = tensor([1])]; + tensor attn_mask_59 = expand_dims(axes = attn_mask_59_axes_0, x = attn_mask_57)[name = tensor("attn_mask_59")]; + tensor var_6439 = const()[name = tensor("op_6439"), val = tensor([0x1.fffe5cp-4])]; + tensor var_6445_transpose_x_0 = const()[name = tensor("op_6445_transpose_x_0"), val = tensor(false)]; + tensor var_6445_transpose_y_0 = const()[name = tensor("op_6445_transpose_y_0"), val = tensor(false)]; + tensor transpose_100_perm_0 = const()[name = tensor("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = tensor("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = keys_87)[name = tensor("transpose_157")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = q_87)[name = tensor("transpose_158")]; + tensor var_6445 = matmul(transpose_x = var_6445_transpose_x_0, transpose_y = var_6445_transpose_y_0, x = transpose_100, y = transpose_101)[name = tensor("op_6445")]; + tensor attn_weights_85 = mul(x = var_6445, y = var_6439)[name = tensor("attn_weights_85")]; + tensor var_6447 = logical_not(x = attn_mask_59)[name = tensor("op_6447")]; + tensor var_6448 = const()[name = tensor("op_6448"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_87 = select(a = var_6448, b = attn_weights_85, cond = var_6447)[name = tensor("attn_weights_87")]; + tensor var_6450 = const()[name = tensor("op_6450"), val = tensor(-1)]; + tensor attn_weights_89 = softmax(axis = var_6450, x = attn_weights_87)[name = tensor("attn_weights_89")]; + tensor attn_output_29_transpose_x_0 = const()[name = tensor("attn_output_29_transpose_x_0"), val = tensor(false)]; + tensor attn_output_29_transpose_y_0 = const()[name = tensor("attn_output_29_transpose_y_0"), val = tensor(false)]; + tensor values_89 = transpose(perm = var_6396, x = values_87)[name = tensor("transpose_159")]; + tensor attn_output_29 = matmul(transpose_x = attn_output_29_transpose_x_0, transpose_y = attn_output_29_transpose_y_0, x = attn_weights_89, y = values_89)[name = tensor("attn_output_29")]; + tensor var_6458 = const()[name = tensor("op_6458"), val = tensor([0, 2, 1, 3])]; + tensor var_6461 = const()[name = tensor("op_6461"), val = tensor([1, 1, 1024])]; + tensor var_6459 = transpose(perm = var_6458, x = attn_output_29)[name = tensor("transpose_156")]; + tensor input_145 = reshape(shape = var_6461, x = var_6459)[name = tensor("input_145")]; + tensor attn_out_29 = linear(bias = linear_0_bias_0, weight = attn14_out_proj_weight, x = input_145)[name = tensor("linear_58")]; + tensor var_6467 = const()[name = tensor("op_6467"), val = tensor(0x1p+0)]; + tensor var_6468 = add(x = position14, y = var_6467)[name = tensor("op_6468")]; + tensor input_147 = add(x = input_143, y = attn_out_29)[name = tensor("input_147")]; + tensor var_6472 = const()[name = tensor("op_6472"), val = tensor(0x1.4f8b58p-17)]; + tensor input_149_axes_0 = const()[name = tensor("input_149_axes_0"), val = tensor([-1])]; + tensor input_149 = layer_norm(axes = input_149_axes_0, beta = norm14_2_bias, epsilon = var_6472, gamma = norm14_2_weight, x = input_147)[name = tensor("input_149")]; + tensor var_6480 = linear(bias = linear_3_bias_0, weight = linear14_1_weight, x = input_149)[name = tensor("linear_59")]; + tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("EXACT")]; + tensor input_151 = gelu(mode = input_151_mode_0, x = var_6480)[name = tensor("input_151")]; + tensor ffn_out_29 = linear(bias = linear_0_bias_0, weight = linear14_2_weight, x = input_151)[name = tensor("linear_60")]; + tensor input_153 = add(x = input_147, y = ffn_out_29)[name = tensor("input_153")]; + tensor var_6489 = const()[name = tensor("op_6489"), val = tensor(0x1.4f8b58p-17)]; + tensor x_31_axes_0 = const()[name = tensor("x_31_axes_0"), val = tensor([-1])]; + tensor x_31 = layer_norm(axes = x_31_axes_0, beta = norm15_1_bias, epsilon = var_6489, gamma = norm15_1_weight, x = input_153)[name = tensor("x_31")]; + tensor var_6521 = linear(bias = linear_1_bias_0, weight = attn15_in_proj_weight, x = x_31)[name = tensor("linear_61")]; + tensor var_6525 = const()[name = tensor("op_6525"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_31 = reshape(shape = var_6525, x = var_6521)[name = tensor("qkv_31")]; + tensor q_91_begin_0 = const()[name = tensor("q_91_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_91_end_0 = const()[name = tensor("q_91_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_91_end_mask_0 = const()[name = tensor("q_91_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_91_squeeze_mask_0 = const()[name = tensor("q_91_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_91 = slice_by_index(begin = q_91_begin_0, end = q_91_end_0, end_mask = q_91_end_mask_0, squeeze_mask = q_91_squeeze_mask_0, x = qkv_31)[name = tensor("q_91")]; + tensor k_61_begin_0 = const()[name = tensor("k_61_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_61_end_0 = const()[name = tensor("k_61_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_61_end_mask_0 = const()[name = tensor("k_61_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_61_squeeze_mask_0 = const()[name = tensor("k_61_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_61 = slice_by_index(begin = k_61_begin_0, end = k_61_end_0, end_mask = k_61_end_mask_0, squeeze_mask = k_61_squeeze_mask_0, x = qkv_31)[name = tensor("k_61")]; + tensor v_31_begin_0 = const()[name = tensor("v_31_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_31_end_0 = const()[name = tensor("v_31_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_31_end_mask_0 = const()[name = tensor("v_31_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_31_squeeze_mask_0 = const()[name = tensor("v_31_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_31 = slice_by_index(begin = v_31_begin_0, end = v_31_end_0, end_mask = v_31_end_mask_0, squeeze_mask = v_31_squeeze_mask_0, x = qkv_31)[name = tensor("v_31")]; + tensor freqs_31 = const()[name = tensor("freqs_31"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210643904)))]; + tensor var_6629 = const()[name = tensor("op_6629"), val = tensor([1, 1, 1, 1])]; + tensor ts_95 = reshape(shape = var_6629, x = position15)[name = tensor("ts_95")]; + tensor var_6633 = const()[name = tensor("op_6633"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_31 = reshape(shape = var_6633, x = q_91)[name = tensor("q_complex_31")]; + tensor var_6637 = const()[name = tensor("op_6637"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_31 = reshape(shape = var_6637, x = k_61)[name = tensor("k_complex_31")]; + tensor var_6641_begin_0 = const()[name = tensor("op_6641_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6641_end_0 = const()[name = tensor("op_6641_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6641_end_mask_0 = const()[name = tensor("op_6641_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6641_squeeze_mask_0 = const()[name = tensor("op_6641_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6641 = slice_by_index(begin = var_6641_begin_0, end = var_6641_end_0, end_mask = var_6641_end_mask_0, squeeze_mask = var_6641_squeeze_mask_0, x = q_complex_31)[name = tensor("op_6641")]; + tensor var_6649_begin_0 = const()[name = tensor("op_6649_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6649_end_0 = const()[name = tensor("op_6649_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6649_end_mask_0 = const()[name = tensor("op_6649_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6649_squeeze_mask_0 = const()[name = tensor("op_6649_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6649 = slice_by_index(begin = var_6649_begin_0, end = var_6649_end_0, end_mask = var_6649_end_mask_0, squeeze_mask = var_6649_squeeze_mask_0, x = q_complex_31)[name = tensor("op_6649")]; + tensor var_6657_begin_0 = const()[name = tensor("op_6657_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6657_end_0 = const()[name = tensor("op_6657_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_6657_end_mask_0 = const()[name = tensor("op_6657_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6657_squeeze_mask_0 = const()[name = tensor("op_6657_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6657 = slice_by_index(begin = var_6657_begin_0, end = var_6657_end_0, end_mask = var_6657_end_mask_0, squeeze_mask = var_6657_squeeze_mask_0, x = k_complex_31)[name = tensor("op_6657")]; + tensor var_6665_begin_0 = const()[name = tensor("op_6665_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_6665_end_0 = const()[name = tensor("op_6665_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_6665_end_mask_0 = const()[name = tensor("op_6665_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_6665_squeeze_mask_0 = const()[name = tensor("op_6665_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_6665 = slice_by_index(begin = var_6665_begin_0, end = var_6665_end_0, end_mask = var_6665_end_mask_0, squeeze_mask = var_6665_squeeze_mask_0, x = k_complex_31)[name = tensor("op_6665")]; + tensor var_6671 = mul(x = freqs_31, y = ts_95)[name = tensor("op_6671")]; + tensor rotr_31 = cos(x = var_6671)[name = tensor("rotr_31")]; + tensor roti_31 = sin(x = var_6671)[name = tensor("roti_31")]; + tensor var_6675 = mul(x = var_6641, y = rotr_31)[name = tensor("op_6675")]; + tensor var_6676 = mul(x = var_6649, y = roti_31)[name = tensor("op_6676")]; + tensor qor_61 = sub(x = var_6675, y = var_6676)[name = tensor("qor_61")]; + tensor var_6679 = mul(x = var_6641, y = roti_31)[name = tensor("op_6679")]; + tensor var_6680 = mul(x = var_6649, y = rotr_31)[name = tensor("op_6680")]; + tensor qoi_61 = add(x = var_6679, y = var_6680)[name = tensor("qoi_61")]; + tensor var_6683 = mul(x = var_6657, y = rotr_31)[name = tensor("op_6683")]; + tensor var_6684 = mul(x = var_6665, y = roti_31)[name = tensor("op_6684")]; + tensor kor_61 = sub(x = var_6683, y = var_6684)[name = tensor("kor_61")]; + tensor var_6687 = mul(x = var_6657, y = roti_31)[name = tensor("op_6687")]; + tensor var_6688 = mul(x = var_6665, y = rotr_31)[name = tensor("op_6688")]; + tensor koi_61 = add(x = var_6687, y = var_6688)[name = tensor("koi_61")]; + tensor qo_31_axis_0 = const()[name = tensor("qo_31_axis_0"), val = tensor(-1)]; + tensor qo_31 = stack(axis = qo_31_axis_0, values = (qor_61, qoi_61))[name = tensor("qo_31")]; + tensor ko_31_axis_0 = const()[name = tensor("ko_31_axis_0"), val = tensor(-1)]; + tensor ko_31 = stack(axis = ko_31_axis_0, values = (kor_61, koi_61))[name = tensor("ko_31")]; + tensor var_6717 = const()[name = tensor("op_6717"), val = tensor([1, 1, 16, 64])]; + tensor q_93 = reshape(shape = var_6717, x = qo_31)[name = tensor("q_93")]; + tensor var_6719 = const()[name = tensor("op_6719"), val = tensor([1, 1, 16, 64])]; + tensor k_63 = reshape(shape = var_6719, x = ko_31)[name = tensor("k_63")]; + tensor _inversed_6741_y_0 = const()[name = tensor("_inversed_6741_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_6741 = mul(x = ts_95, y = _inversed_6741_y_0)[name = tensor("_inversed_6741")]; + tensor var_6742 = floor(x = _inversed_6741)[name = tensor("op_6742")]; + tensor var_6743 = const()[name = tensor("op_6743"), val = tensor(0x1p+9)]; + tensor var_6744 = mul(x = var_6742, y = var_6743)[name = tensor("op_6744")]; + tensor write_indices_float_63 = sub(x = ts_95, y = var_6744)[name = tensor("write_indices_float_63")]; + tensor var_6751_dtype_0 = const()[name = tensor("op_6751_dtype_0"), val = tensor("int32")]; + tensor write_indices_31_reps_0 = const()[name = tensor("write_indices_31_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_6751 = cast(dtype = var_6751_dtype_0, x = write_indices_float_63)[name = tensor("cast_440")]; + tensor write_indices_31 = tile(reps = write_indices_31_reps_0, x = var_6751)[name = tensor("write_indices_31")]; + tensor var_6759_begin_0 = const()[name = tensor("op_6759_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_6759_end_0 = const()[name = tensor("op_6759_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_6759_end_mask_0 = const()[name = tensor("op_6759_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6759_squeeze_mask_0 = const()[name = tensor("op_6759_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6759 = slice_by_index(begin = var_6759_begin_0, end = var_6759_end_0, end_mask = var_6759_end_mask_0, squeeze_mask = var_6759_squeeze_mask_0, x = cache15)[name = tensor("op_6759")]; + tensor var_6761_axis_0 = const()[name = tensor("op_6761_axis_0"), val = tensor(1)]; + tensor var_6761_mode_0 = const()[name = tensor("op_6761_mode_0"), val = tensor("update")]; + tensor var_6761_validate_indices_0 = const()[name = tensor("op_6761_validate_indices_0"), val = tensor(false)]; + tensor var_6761 = scatter_along_axis(axis = var_6761_axis_0, data = var_6759, indices = write_indices_31, mode = var_6761_mode_0, updates = k_63, validate_indices = var_6761_validate_indices_0)[name = tensor("op_6761")]; + tensor concat_107 = const()[name = tensor("concat_107"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_108 = const()[name = tensor("concat_108"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_31_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_31_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_78 = const()[name = tensor("shape_78"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_30 = const()[name = tensor("reduce_prod_30"), val = tensor(1048576)]; + tensor range_1d_30_start_0 = const()[name = tensor("range_1d_30_start_0"), val = tensor(0)]; + tensor range_1d_30_step_0 = const()[name = tensor("range_1d_30_step_0"), val = tensor(1)]; + tensor range_1d_30 = range_1d(end = reduce_prod_30, start = range_1d_30_start_0, step = range_1d_30_step_0)[name = tensor("range_1d_30")]; + tensor reshape_150 = reshape(shape = shape_78, x = range_1d_30)[name = tensor("reshape_150")]; + tensor slice_by_index_30 = slice_by_index(begin = concat_107, begin_mask = new_cache_31_internal_tensor_assign_1_begin_mask_0, end = concat_108, end_mask = new_cache_31_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_31_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_31_internal_tensor_assign_1_stride_0, x = reshape_150)[name = tensor("slice_by_index_30")]; + tensor reshape_151_shape_0 = const()[name = tensor("reshape_151_shape_0"), val = tensor([-1])]; + tensor reshape_151 = reshape(shape = reshape_151_shape_0, x = slice_by_index_30)[name = tensor("reshape_151")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([-1])]; + tensor reshape_152 = reshape(shape = reshape_152_shape_0, x = var_6761)[name = tensor("reshape_152")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([-1])]; + tensor reshape_153 = reshape(shape = reshape_153_shape_0, x = cache15)[name = tensor("reshape_153")]; + tensor scatter_30_mode_0 = const()[name = tensor("scatter_30_mode_0"), val = tensor("update")]; + tensor scatter_30_axis_0 = const()[name = tensor("scatter_30_axis_0"), val = tensor(0)]; + tensor scatter_30_validate_indices_0 = const()[name = tensor("scatter_30_validate_indices_0"), val = tensor(false)]; + tensor scatter_30 = scatter(axis = scatter_30_axis_0, data = reshape_153, indices = reshape_151, mode = scatter_30_mode_0, updates = reshape_152, validate_indices = scatter_30_validate_indices_0)[name = tensor("scatter_30")]; + tensor reshape_154 = reshape(shape = shape_78, x = scatter_30)[name = tensor("reshape_154")]; + tensor var_6769_begin_0 = const()[name = tensor("op_6769_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_6769_end_0 = const()[name = tensor("op_6769_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_6769_end_mask_0 = const()[name = tensor("op_6769_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_6769_squeeze_mask_0 = const()[name = tensor("op_6769_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_6769 = slice_by_index(begin = var_6769_begin_0, end = var_6769_end_0, end_mask = var_6769_end_mask_0, squeeze_mask = var_6769_squeeze_mask_0, x = reshape_154)[name = tensor("op_6769")]; + tensor var_6771_axis_0 = const()[name = tensor("op_6771_axis_0"), val = tensor(1)]; + tensor var_6771_mode_0 = const()[name = tensor("op_6771_mode_0"), val = tensor("update")]; + tensor var_6771_validate_indices_0 = const()[name = tensor("op_6771_validate_indices_0"), val = tensor(false)]; + tensor var_6771 = scatter_along_axis(axis = var_6771_axis_0, data = var_6769, indices = write_indices_31, mode = var_6771_mode_0, updates = v_31, validate_indices = var_6771_validate_indices_0)[name = tensor("op_6771")]; + tensor concat_109 = const()[name = tensor("concat_109"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_110 = const()[name = tensor("concat_110"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_31_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_31_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_31_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_31_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_79 = const()[name = tensor("shape_79"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_31 = const()[name = tensor("reduce_prod_31"), val = tensor(1048576)]; + tensor range_1d_31_start_0 = const()[name = tensor("range_1d_31_start_0"), val = tensor(0)]; + tensor range_1d_31_step_0 = const()[name = tensor("range_1d_31_step_0"), val = tensor(1)]; + tensor range_1d_31 = range_1d(end = reduce_prod_31, start = range_1d_31_start_0, step = range_1d_31_step_0)[name = tensor("range_1d_31")]; + tensor reshape_155 = reshape(shape = shape_79, x = range_1d_31)[name = tensor("reshape_155")]; + tensor slice_by_index_31 = slice_by_index(begin = concat_109, begin_mask = new_cache_31_internal_tensor_assign_2_begin_mask_0, end = concat_110, end_mask = new_cache_31_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_31_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_31_internal_tensor_assign_2_stride_0, x = reshape_155)[name = tensor("slice_by_index_31")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([-1])]; + tensor reshape_156 = reshape(shape = reshape_156_shape_0, x = slice_by_index_31)[name = tensor("reshape_156")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([-1])]; + tensor reshape_157 = reshape(shape = reshape_157_shape_0, x = var_6771)[name = tensor("reshape_157")]; + tensor reshape_158_shape_0 = const()[name = tensor("reshape_158_shape_0"), val = tensor([-1])]; + tensor reshape_158 = reshape(shape = reshape_158_shape_0, x = reshape_154)[name = tensor("reshape_158")]; + tensor scatter_31_mode_0 = const()[name = tensor("scatter_31_mode_0"), val = tensor("update")]; + tensor scatter_31_axis_0 = const()[name = tensor("scatter_31_axis_0"), val = tensor(0)]; + tensor scatter_31_validate_indices_0 = const()[name = tensor("scatter_31_validate_indices_0"), val = tensor(false)]; + tensor scatter_31 = scatter(axis = scatter_31_axis_0, data = reshape_158, indices = reshape_156, mode = scatter_31_mode_0, updates = reshape_157, validate_indices = scatter_31_validate_indices_0)[name = tensor("scatter_31")]; + tensor new_cache_31_internal_tensor_assign_2 = reshape(shape = shape_79, x = scatter_31)[name = tensor("reshape_159")]; + tensor keys_91_begin_0 = const()[name = tensor("keys_91_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_91_end_0 = const()[name = tensor("keys_91_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_91_end_mask_0 = const()[name = tensor("keys_91_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_91_squeeze_mask_0 = const()[name = tensor("keys_91_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_91 = slice_by_index(begin = keys_91_begin_0, end = keys_91_end_0, end_mask = keys_91_end_mask_0, squeeze_mask = keys_91_squeeze_mask_0, x = new_cache_31_internal_tensor_assign_2)[name = tensor("keys_91")]; + tensor values_91_begin_0 = const()[name = tensor("values_91_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_91_end_0 = const()[name = tensor("values_91_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_91_end_mask_0 = const()[name = tensor("values_91_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_91_squeeze_mask_0 = const()[name = tensor("values_91_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_91 = slice_by_index(begin = values_91_begin_0, end = values_91_end_0, end_mask = values_91_end_mask_0, squeeze_mask = values_91_squeeze_mask_0, x = new_cache_31_internal_tensor_assign_2)[name = tensor("values_91")]; + tensor var_6783 = not_equal(x = keys_91, y = keys_91)[name = tensor("op_6783")]; + tensor keys_93 = select(a = var_504, b = keys_91, cond = var_6783)[name = tensor("keys_93")]; + tensor var_6791 = not_equal(x = values_91, y = values_91)[name = tensor("op_6791")]; + tensor values_93 = select(a = var_504, b = values_91, cond = var_6791)[name = tensor("values_93")]; + tensor var_6815 = const()[name = tensor("op_6815"), val = tensor([0, 2, 1, 3])]; + tensor var_6828 = const()[name = tensor("op_6828"), val = tensor([1, 1, 1])]; + tensor var_6829 = reshape(shape = var_6828, x = position15)[name = tensor("op_6829")]; + tensor var_6846 = const()[name = tensor("op_6846"), val = tensor(0x1p+0)]; + tensor valid_len_31 = add(x = var_6829, y = var_6846)[name = tensor("valid_len_31")]; + tensor valid_mask_31 = less(x = k_positions_1_promoted, y = valid_len_31)[name = tensor("valid_mask_31")]; + tensor causal_mask_31 = less_equal(x = k_positions_1_promoted, y = var_6829)[name = tensor("causal_mask_31")]; + tensor attn_mask_61 = logical_and(x = valid_mask_31, y = causal_mask_31)[name = tensor("attn_mask_61")]; + tensor attn_mask_63_axes_0 = const()[name = tensor("attn_mask_63_axes_0"), val = tensor([1])]; + tensor attn_mask_63 = expand_dims(axes = attn_mask_63_axes_0, x = attn_mask_61)[name = tensor("attn_mask_63")]; + tensor var_6858 = const()[name = tensor("op_6858"), val = tensor([0x1.fffe5cp-4])]; + tensor var_6864_transpose_x_0 = const()[name = tensor("op_6864_transpose_x_0"), val = tensor(false)]; + tensor var_6864_transpose_y_0 = const()[name = tensor("op_6864_transpose_y_0"), val = tensor(false)]; + tensor transpose_102_perm_0 = const()[name = tensor("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = tensor("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = keys_93)[name = tensor("transpose_153")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = q_93)[name = tensor("transpose_154")]; + tensor var_6864 = matmul(transpose_x = var_6864_transpose_x_0, transpose_y = var_6864_transpose_y_0, x = transpose_102, y = transpose_103)[name = tensor("op_6864")]; + tensor attn_weights_91 = mul(x = var_6864, y = var_6858)[name = tensor("attn_weights_91")]; + tensor var_6866 = logical_not(x = attn_mask_63)[name = tensor("op_6866")]; + tensor var_6867 = const()[name = tensor("op_6867"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_93 = select(a = var_6867, b = attn_weights_91, cond = var_6866)[name = tensor("attn_weights_93")]; + tensor var_6869 = const()[name = tensor("op_6869"), val = tensor(-1)]; + tensor attn_weights_95 = softmax(axis = var_6869, x = attn_weights_93)[name = tensor("attn_weights_95")]; + tensor attn_output_31_transpose_x_0 = const()[name = tensor("attn_output_31_transpose_x_0"), val = tensor(false)]; + tensor attn_output_31_transpose_y_0 = const()[name = tensor("attn_output_31_transpose_y_0"), val = tensor(false)]; + tensor values_95 = transpose(perm = var_6815, x = values_93)[name = tensor("transpose_155")]; + tensor attn_output_31 = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = attn_weights_95, y = values_95)[name = tensor("attn_output_31")]; + tensor var_6877 = const()[name = tensor("op_6877"), val = tensor([0, 2, 1, 3])]; + tensor var_6880 = const()[name = tensor("op_6880"), val = tensor([1, 1, 1024])]; + tensor var_6878 = transpose(perm = var_6877, x = attn_output_31)[name = tensor("transpose_152")]; + tensor input_155 = reshape(shape = var_6880, x = var_6878)[name = tensor("input_155")]; + tensor attn_out_31 = linear(bias = linear_0_bias_0, weight = attn15_out_proj_weight, x = input_155)[name = tensor("linear_62")]; + tensor var_6886 = const()[name = tensor("op_6886"), val = tensor(0x1p+0)]; + tensor var_6887 = add(x = position15, y = var_6886)[name = tensor("op_6887")]; + tensor input_157 = add(x = input_153, y = attn_out_31)[name = tensor("input_157")]; + tensor var_6891 = const()[name = tensor("op_6891"), val = tensor(0x1.4f8b58p-17)]; + tensor input_159_axes_0 = const()[name = tensor("input_159_axes_0"), val = tensor([-1])]; + tensor input_159 = layer_norm(axes = input_159_axes_0, beta = norm15_2_bias, epsilon = var_6891, gamma = norm15_2_weight, x = input_157)[name = tensor("input_159")]; + tensor var_6899 = linear(bias = linear_3_bias_0, weight = linear15_1_weight, x = input_159)[name = tensor("linear_63")]; + tensor input_161_mode_0 = const()[name = tensor("input_161_mode_0"), val = tensor("EXACT")]; + tensor input_161 = gelu(mode = input_161_mode_0, x = var_6899)[name = tensor("input_161")]; + tensor ffn_out_31 = linear(bias = linear_0_bias_0, weight = linear15_2_weight, x = input_161)[name = tensor("linear_64")]; + tensor input_163 = add(x = input_157, y = ffn_out_31)[name = tensor("input_163")]; + tensor var_6908 = const()[name = tensor("op_6908"), val = tensor(0x1.4f8b58p-17)]; + tensor x_33_axes_0 = const()[name = tensor("x_33_axes_0"), val = tensor([-1])]; + tensor x_33 = layer_norm(axes = x_33_axes_0, beta = norm16_1_bias, epsilon = var_6908, gamma = norm16_1_weight, x = input_163)[name = tensor("x_33")]; + tensor var_6940 = linear(bias = linear_1_bias_0, weight = attn16_in_proj_weight, x = x_33)[name = tensor("linear_65")]; + tensor var_6944 = const()[name = tensor("op_6944"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_33 = reshape(shape = var_6944, x = var_6940)[name = tensor("qkv_33")]; + tensor q_97_begin_0 = const()[name = tensor("q_97_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_97_end_0 = const()[name = tensor("q_97_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_97_end_mask_0 = const()[name = tensor("q_97_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_97_squeeze_mask_0 = const()[name = tensor("q_97_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_97 = slice_by_index(begin = q_97_begin_0, end = q_97_end_0, end_mask = q_97_end_mask_0, squeeze_mask = q_97_squeeze_mask_0, x = qkv_33)[name = tensor("q_97")]; + tensor k_65_begin_0 = const()[name = tensor("k_65_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_65_end_0 = const()[name = tensor("k_65_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_65_end_mask_0 = const()[name = tensor("k_65_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_65_squeeze_mask_0 = const()[name = tensor("k_65_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_65 = slice_by_index(begin = k_65_begin_0, end = k_65_end_0, end_mask = k_65_end_mask_0, squeeze_mask = k_65_squeeze_mask_0, x = qkv_33)[name = tensor("k_65")]; + tensor v_33_begin_0 = const()[name = tensor("v_33_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_33_end_0 = const()[name = tensor("v_33_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_33_end_mask_0 = const()[name = tensor("v_33_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_33_squeeze_mask_0 = const()[name = tensor("v_33_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_33 = slice_by_index(begin = v_33_begin_0, end = v_33_end_0, end_mask = v_33_end_mask_0, squeeze_mask = v_33_squeeze_mask_0, x = qkv_33)[name = tensor("v_33")]; + tensor freqs_33 = const()[name = tensor("freqs_33"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210644096)))]; + tensor var_7048 = const()[name = tensor("op_7048"), val = tensor([1, 1, 1, 1])]; + tensor ts_101 = reshape(shape = var_7048, x = position16)[name = tensor("ts_101")]; + tensor var_7052 = const()[name = tensor("op_7052"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_33 = reshape(shape = var_7052, x = q_97)[name = tensor("q_complex_33")]; + tensor var_7056 = const()[name = tensor("op_7056"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_33 = reshape(shape = var_7056, x = k_65)[name = tensor("k_complex_33")]; + tensor var_7060_begin_0 = const()[name = tensor("op_7060_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7060_end_0 = const()[name = tensor("op_7060_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7060_end_mask_0 = const()[name = tensor("op_7060_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7060_squeeze_mask_0 = const()[name = tensor("op_7060_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7060 = slice_by_index(begin = var_7060_begin_0, end = var_7060_end_0, end_mask = var_7060_end_mask_0, squeeze_mask = var_7060_squeeze_mask_0, x = q_complex_33)[name = tensor("op_7060")]; + tensor var_7068_begin_0 = const()[name = tensor("op_7068_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7068_end_0 = const()[name = tensor("op_7068_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7068_end_mask_0 = const()[name = tensor("op_7068_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7068_squeeze_mask_0 = const()[name = tensor("op_7068_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7068 = slice_by_index(begin = var_7068_begin_0, end = var_7068_end_0, end_mask = var_7068_end_mask_0, squeeze_mask = var_7068_squeeze_mask_0, x = q_complex_33)[name = tensor("op_7068")]; + tensor var_7076_begin_0 = const()[name = tensor("op_7076_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7076_end_0 = const()[name = tensor("op_7076_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7076_end_mask_0 = const()[name = tensor("op_7076_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7076_squeeze_mask_0 = const()[name = tensor("op_7076_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7076 = slice_by_index(begin = var_7076_begin_0, end = var_7076_end_0, end_mask = var_7076_end_mask_0, squeeze_mask = var_7076_squeeze_mask_0, x = k_complex_33)[name = tensor("op_7076")]; + tensor var_7084_begin_0 = const()[name = tensor("op_7084_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7084_end_0 = const()[name = tensor("op_7084_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7084_end_mask_0 = const()[name = tensor("op_7084_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7084_squeeze_mask_0 = const()[name = tensor("op_7084_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7084 = slice_by_index(begin = var_7084_begin_0, end = var_7084_end_0, end_mask = var_7084_end_mask_0, squeeze_mask = var_7084_squeeze_mask_0, x = k_complex_33)[name = tensor("op_7084")]; + tensor var_7090 = mul(x = freqs_33, y = ts_101)[name = tensor("op_7090")]; + tensor rotr_33 = cos(x = var_7090)[name = tensor("rotr_33")]; + tensor roti_33 = sin(x = var_7090)[name = tensor("roti_33")]; + tensor var_7094 = mul(x = var_7060, y = rotr_33)[name = tensor("op_7094")]; + tensor var_7095 = mul(x = var_7068, y = roti_33)[name = tensor("op_7095")]; + tensor qor_65 = sub(x = var_7094, y = var_7095)[name = tensor("qor_65")]; + tensor var_7098 = mul(x = var_7060, y = roti_33)[name = tensor("op_7098")]; + tensor var_7099 = mul(x = var_7068, y = rotr_33)[name = tensor("op_7099")]; + tensor qoi_65 = add(x = var_7098, y = var_7099)[name = tensor("qoi_65")]; + tensor var_7102 = mul(x = var_7076, y = rotr_33)[name = tensor("op_7102")]; + tensor var_7103 = mul(x = var_7084, y = roti_33)[name = tensor("op_7103")]; + tensor kor_65 = sub(x = var_7102, y = var_7103)[name = tensor("kor_65")]; + tensor var_7106 = mul(x = var_7076, y = roti_33)[name = tensor("op_7106")]; + tensor var_7107 = mul(x = var_7084, y = rotr_33)[name = tensor("op_7107")]; + tensor koi_65 = add(x = var_7106, y = var_7107)[name = tensor("koi_65")]; + tensor qo_33_axis_0 = const()[name = tensor("qo_33_axis_0"), val = tensor(-1)]; + tensor qo_33 = stack(axis = qo_33_axis_0, values = (qor_65, qoi_65))[name = tensor("qo_33")]; + tensor ko_33_axis_0 = const()[name = tensor("ko_33_axis_0"), val = tensor(-1)]; + tensor ko_33 = stack(axis = ko_33_axis_0, values = (kor_65, koi_65))[name = tensor("ko_33")]; + tensor var_7136 = const()[name = tensor("op_7136"), val = tensor([1, 1, 16, 64])]; + tensor q_99 = reshape(shape = var_7136, x = qo_33)[name = tensor("q_99")]; + tensor var_7138 = const()[name = tensor("op_7138"), val = tensor([1, 1, 16, 64])]; + tensor k_67 = reshape(shape = var_7138, x = ko_33)[name = tensor("k_67")]; + tensor _inversed_7160_y_0 = const()[name = tensor("_inversed_7160_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_7160 = mul(x = ts_101, y = _inversed_7160_y_0)[name = tensor("_inversed_7160")]; + tensor var_7161 = floor(x = _inversed_7160)[name = tensor("op_7161")]; + tensor var_7162 = const()[name = tensor("op_7162"), val = tensor(0x1p+9)]; + tensor var_7163 = mul(x = var_7161, y = var_7162)[name = tensor("op_7163")]; + tensor write_indices_float_67 = sub(x = ts_101, y = var_7163)[name = tensor("write_indices_float_67")]; + tensor var_7170_dtype_0 = const()[name = tensor("op_7170_dtype_0"), val = tensor("int32")]; + tensor write_indices_33_reps_0 = const()[name = tensor("write_indices_33_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_7170 = cast(dtype = var_7170_dtype_0, x = write_indices_float_67)[name = tensor("cast_439")]; + tensor write_indices_33 = tile(reps = write_indices_33_reps_0, x = var_7170)[name = tensor("write_indices_33")]; + tensor var_7178_begin_0 = const()[name = tensor("op_7178_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7178_end_0 = const()[name = tensor("op_7178_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_7178_end_mask_0 = const()[name = tensor("op_7178_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7178_squeeze_mask_0 = const()[name = tensor("op_7178_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7178 = slice_by_index(begin = var_7178_begin_0, end = var_7178_end_0, end_mask = var_7178_end_mask_0, squeeze_mask = var_7178_squeeze_mask_0, x = cache16)[name = tensor("op_7178")]; + tensor var_7180_axis_0 = const()[name = tensor("op_7180_axis_0"), val = tensor(1)]; + tensor var_7180_mode_0 = const()[name = tensor("op_7180_mode_0"), val = tensor("update")]; + tensor var_7180_validate_indices_0 = const()[name = tensor("op_7180_validate_indices_0"), val = tensor(false)]; + tensor var_7180 = scatter_along_axis(axis = var_7180_axis_0, data = var_7178, indices = write_indices_33, mode = var_7180_mode_0, updates = k_67, validate_indices = var_7180_validate_indices_0)[name = tensor("op_7180")]; + tensor concat_114 = const()[name = tensor("concat_114"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_115 = const()[name = tensor("concat_115"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_33_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_33_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_80 = const()[name = tensor("shape_80"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_32 = const()[name = tensor("reduce_prod_32"), val = tensor(1048576)]; + tensor range_1d_32_start_0 = const()[name = tensor("range_1d_32_start_0"), val = tensor(0)]; + tensor range_1d_32_step_0 = const()[name = tensor("range_1d_32_step_0"), val = tensor(1)]; + tensor range_1d_32 = range_1d(end = reduce_prod_32, start = range_1d_32_start_0, step = range_1d_32_step_0)[name = tensor("range_1d_32")]; + tensor reshape_160 = reshape(shape = shape_80, x = range_1d_32)[name = tensor("reshape_160")]; + tensor slice_by_index_32 = slice_by_index(begin = concat_114, begin_mask = new_cache_33_internal_tensor_assign_1_begin_mask_0, end = concat_115, end_mask = new_cache_33_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_33_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_33_internal_tensor_assign_1_stride_0, x = reshape_160)[name = tensor("slice_by_index_32")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([-1])]; + tensor reshape_161 = reshape(shape = reshape_161_shape_0, x = slice_by_index_32)[name = tensor("reshape_161")]; + tensor reshape_162_shape_0 = const()[name = tensor("reshape_162_shape_0"), val = tensor([-1])]; + tensor reshape_162 = reshape(shape = reshape_162_shape_0, x = var_7180)[name = tensor("reshape_162")]; + tensor reshape_163_shape_0 = const()[name = tensor("reshape_163_shape_0"), val = tensor([-1])]; + tensor reshape_163 = reshape(shape = reshape_163_shape_0, x = cache16)[name = tensor("reshape_163")]; + tensor scatter_32_mode_0 = const()[name = tensor("scatter_32_mode_0"), val = tensor("update")]; + tensor scatter_32_axis_0 = const()[name = tensor("scatter_32_axis_0"), val = tensor(0)]; + tensor scatter_32_validate_indices_0 = const()[name = tensor("scatter_32_validate_indices_0"), val = tensor(false)]; + tensor scatter_32 = scatter(axis = scatter_32_axis_0, data = reshape_163, indices = reshape_161, mode = scatter_32_mode_0, updates = reshape_162, validate_indices = scatter_32_validate_indices_0)[name = tensor("scatter_32")]; + tensor reshape_164 = reshape(shape = shape_80, x = scatter_32)[name = tensor("reshape_164")]; + tensor var_7188_begin_0 = const()[name = tensor("op_7188_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_7188_end_0 = const()[name = tensor("op_7188_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_7188_end_mask_0 = const()[name = tensor("op_7188_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7188_squeeze_mask_0 = const()[name = tensor("op_7188_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7188 = slice_by_index(begin = var_7188_begin_0, end = var_7188_end_0, end_mask = var_7188_end_mask_0, squeeze_mask = var_7188_squeeze_mask_0, x = reshape_164)[name = tensor("op_7188")]; + tensor var_7190_axis_0 = const()[name = tensor("op_7190_axis_0"), val = tensor(1)]; + tensor var_7190_mode_0 = const()[name = tensor("op_7190_mode_0"), val = tensor("update")]; + tensor var_7190_validate_indices_0 = const()[name = tensor("op_7190_validate_indices_0"), val = tensor(false)]; + tensor var_7190 = scatter_along_axis(axis = var_7190_axis_0, data = var_7188, indices = write_indices_33, mode = var_7190_mode_0, updates = v_33, validate_indices = var_7190_validate_indices_0)[name = tensor("op_7190")]; + tensor concat_116 = const()[name = tensor("concat_116"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_117 = const()[name = tensor("concat_117"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_33_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_33_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_33_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_33_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_81 = const()[name = tensor("shape_81"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_33 = const()[name = tensor("reduce_prod_33"), val = tensor(1048576)]; + tensor range_1d_33_start_0 = const()[name = tensor("range_1d_33_start_0"), val = tensor(0)]; + tensor range_1d_33_step_0 = const()[name = tensor("range_1d_33_step_0"), val = tensor(1)]; + tensor range_1d_33 = range_1d(end = reduce_prod_33, start = range_1d_33_start_0, step = range_1d_33_step_0)[name = tensor("range_1d_33")]; + tensor reshape_165 = reshape(shape = shape_81, x = range_1d_33)[name = tensor("reshape_165")]; + tensor slice_by_index_33 = slice_by_index(begin = concat_116, begin_mask = new_cache_33_internal_tensor_assign_2_begin_mask_0, end = concat_117, end_mask = new_cache_33_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_33_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_33_internal_tensor_assign_2_stride_0, x = reshape_165)[name = tensor("slice_by_index_33")]; + tensor reshape_166_shape_0 = const()[name = tensor("reshape_166_shape_0"), val = tensor([-1])]; + tensor reshape_166 = reshape(shape = reshape_166_shape_0, x = slice_by_index_33)[name = tensor("reshape_166")]; + tensor reshape_167_shape_0 = const()[name = tensor("reshape_167_shape_0"), val = tensor([-1])]; + tensor reshape_167 = reshape(shape = reshape_167_shape_0, x = var_7190)[name = tensor("reshape_167")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([-1])]; + tensor reshape_168 = reshape(shape = reshape_168_shape_0, x = reshape_164)[name = tensor("reshape_168")]; + tensor scatter_33_mode_0 = const()[name = tensor("scatter_33_mode_0"), val = tensor("update")]; + tensor scatter_33_axis_0 = const()[name = tensor("scatter_33_axis_0"), val = tensor(0)]; + tensor scatter_33_validate_indices_0 = const()[name = tensor("scatter_33_validate_indices_0"), val = tensor(false)]; + tensor scatter_33 = scatter(axis = scatter_33_axis_0, data = reshape_168, indices = reshape_166, mode = scatter_33_mode_0, updates = reshape_167, validate_indices = scatter_33_validate_indices_0)[name = tensor("scatter_33")]; + tensor new_cache_33_internal_tensor_assign_2 = reshape(shape = shape_81, x = scatter_33)[name = tensor("reshape_169")]; + tensor keys_97_begin_0 = const()[name = tensor("keys_97_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_97_end_0 = const()[name = tensor("keys_97_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_97_end_mask_0 = const()[name = tensor("keys_97_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_97_squeeze_mask_0 = const()[name = tensor("keys_97_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_97 = slice_by_index(begin = keys_97_begin_0, end = keys_97_end_0, end_mask = keys_97_end_mask_0, squeeze_mask = keys_97_squeeze_mask_0, x = new_cache_33_internal_tensor_assign_2)[name = tensor("keys_97")]; + tensor values_97_begin_0 = const()[name = tensor("values_97_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_97_end_0 = const()[name = tensor("values_97_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_97_end_mask_0 = const()[name = tensor("values_97_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_97_squeeze_mask_0 = const()[name = tensor("values_97_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_97 = slice_by_index(begin = values_97_begin_0, end = values_97_end_0, end_mask = values_97_end_mask_0, squeeze_mask = values_97_squeeze_mask_0, x = new_cache_33_internal_tensor_assign_2)[name = tensor("values_97")]; + tensor var_7202 = not_equal(x = keys_97, y = keys_97)[name = tensor("op_7202")]; + tensor keys_99 = select(a = var_504, b = keys_97, cond = var_7202)[name = tensor("keys_99")]; + tensor var_7210 = not_equal(x = values_97, y = values_97)[name = tensor("op_7210")]; + tensor values_99 = select(a = var_504, b = values_97, cond = var_7210)[name = tensor("values_99")]; + tensor var_7234 = const()[name = tensor("op_7234"), val = tensor([0, 2, 1, 3])]; + tensor var_7247 = const()[name = tensor("op_7247"), val = tensor([1, 1, 1])]; + tensor var_7248 = reshape(shape = var_7247, x = position16)[name = tensor("op_7248")]; + tensor var_7265 = const()[name = tensor("op_7265"), val = tensor(0x1p+0)]; + tensor valid_len_33 = add(x = var_7248, y = var_7265)[name = tensor("valid_len_33")]; + tensor valid_mask_33 = less(x = k_positions_1_promoted, y = valid_len_33)[name = tensor("valid_mask_33")]; + tensor causal_mask_33 = less_equal(x = k_positions_1_promoted, y = var_7248)[name = tensor("causal_mask_33")]; + tensor attn_mask_65 = logical_and(x = valid_mask_33, y = causal_mask_33)[name = tensor("attn_mask_65")]; + tensor attn_mask_67_axes_0 = const()[name = tensor("attn_mask_67_axes_0"), val = tensor([1])]; + tensor attn_mask_67 = expand_dims(axes = attn_mask_67_axes_0, x = attn_mask_65)[name = tensor("attn_mask_67")]; + tensor var_7277 = const()[name = tensor("op_7277"), val = tensor([0x1.fffe5cp-4])]; + tensor var_7283_transpose_x_0 = const()[name = tensor("op_7283_transpose_x_0"), val = tensor(false)]; + tensor var_7283_transpose_y_0 = const()[name = tensor("op_7283_transpose_y_0"), val = tensor(false)]; + tensor transpose_104_perm_0 = const()[name = tensor("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = tensor("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = keys_99)[name = tensor("transpose_149")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = q_99)[name = tensor("transpose_150")]; + tensor var_7283 = matmul(transpose_x = var_7283_transpose_x_0, transpose_y = var_7283_transpose_y_0, x = transpose_104, y = transpose_105)[name = tensor("op_7283")]; + tensor attn_weights_97 = mul(x = var_7283, y = var_7277)[name = tensor("attn_weights_97")]; + tensor var_7285 = logical_not(x = attn_mask_67)[name = tensor("op_7285")]; + tensor var_7286 = const()[name = tensor("op_7286"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_99 = select(a = var_7286, b = attn_weights_97, cond = var_7285)[name = tensor("attn_weights_99")]; + tensor var_7288 = const()[name = tensor("op_7288"), val = tensor(-1)]; + tensor attn_weights_101 = softmax(axis = var_7288, x = attn_weights_99)[name = tensor("attn_weights_101")]; + tensor attn_output_33_transpose_x_0 = const()[name = tensor("attn_output_33_transpose_x_0"), val = tensor(false)]; + tensor attn_output_33_transpose_y_0 = const()[name = tensor("attn_output_33_transpose_y_0"), val = tensor(false)]; + tensor values_101 = transpose(perm = var_7234, x = values_99)[name = tensor("transpose_151")]; + tensor attn_output_33 = matmul(transpose_x = attn_output_33_transpose_x_0, transpose_y = attn_output_33_transpose_y_0, x = attn_weights_101, y = values_101)[name = tensor("attn_output_33")]; + tensor var_7296 = const()[name = tensor("op_7296"), val = tensor([0, 2, 1, 3])]; + tensor var_7299 = const()[name = tensor("op_7299"), val = tensor([1, 1, 1024])]; + tensor var_7297 = transpose(perm = var_7296, x = attn_output_33)[name = tensor("transpose_148")]; + tensor input_165 = reshape(shape = var_7299, x = var_7297)[name = tensor("input_165")]; + tensor attn_out_33 = linear(bias = linear_0_bias_0, weight = attn16_out_proj_weight, x = input_165)[name = tensor("linear_66")]; + tensor var_7305 = const()[name = tensor("op_7305"), val = tensor(0x1p+0)]; + tensor var_7306 = add(x = position16, y = var_7305)[name = tensor("op_7306")]; + tensor input_167 = add(x = input_163, y = attn_out_33)[name = tensor("input_167")]; + tensor var_7310 = const()[name = tensor("op_7310"), val = tensor(0x1.4f8b58p-17)]; + tensor input_169_axes_0 = const()[name = tensor("input_169_axes_0"), val = tensor([-1])]; + tensor input_169 = layer_norm(axes = input_169_axes_0, beta = norm16_2_bias, epsilon = var_7310, gamma = norm16_2_weight, x = input_167)[name = tensor("input_169")]; + tensor var_7318 = linear(bias = linear_3_bias_0, weight = linear16_1_weight, x = input_169)[name = tensor("linear_67")]; + tensor input_171_mode_0 = const()[name = tensor("input_171_mode_0"), val = tensor("EXACT")]; + tensor input_171 = gelu(mode = input_171_mode_0, x = var_7318)[name = tensor("input_171")]; + tensor ffn_out_33 = linear(bias = linear_0_bias_0, weight = linear16_2_weight, x = input_171)[name = tensor("linear_68")]; + tensor input_173 = add(x = input_167, y = ffn_out_33)[name = tensor("input_173")]; + tensor var_7327 = const()[name = tensor("op_7327"), val = tensor(0x1.4f8b58p-17)]; + tensor x_35_axes_0 = const()[name = tensor("x_35_axes_0"), val = tensor([-1])]; + tensor x_35 = layer_norm(axes = x_35_axes_0, beta = norm17_1_bias, epsilon = var_7327, gamma = norm17_1_weight, x = input_173)[name = tensor("x_35")]; + tensor var_7359 = linear(bias = linear_1_bias_0, weight = attn17_in_proj_weight, x = x_35)[name = tensor("linear_69")]; + tensor var_7363 = const()[name = tensor("op_7363"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_35 = reshape(shape = var_7363, x = var_7359)[name = tensor("qkv_35")]; + tensor q_103_begin_0 = const()[name = tensor("q_103_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_103_end_0 = const()[name = tensor("q_103_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_103_end_mask_0 = const()[name = tensor("q_103_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_103_squeeze_mask_0 = const()[name = tensor("q_103_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_103 = slice_by_index(begin = q_103_begin_0, end = q_103_end_0, end_mask = q_103_end_mask_0, squeeze_mask = q_103_squeeze_mask_0, x = qkv_35)[name = tensor("q_103")]; + tensor k_69_begin_0 = const()[name = tensor("k_69_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_69_end_0 = const()[name = tensor("k_69_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_69_end_mask_0 = const()[name = tensor("k_69_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_69_squeeze_mask_0 = const()[name = tensor("k_69_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_69 = slice_by_index(begin = k_69_begin_0, end = k_69_end_0, end_mask = k_69_end_mask_0, squeeze_mask = k_69_squeeze_mask_0, x = qkv_35)[name = tensor("k_69")]; + tensor v_35_begin_0 = const()[name = tensor("v_35_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_35_end_0 = const()[name = tensor("v_35_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_35_end_mask_0 = const()[name = tensor("v_35_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_35_squeeze_mask_0 = const()[name = tensor("v_35_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_35 = slice_by_index(begin = v_35_begin_0, end = v_35_end_0, end_mask = v_35_end_mask_0, squeeze_mask = v_35_squeeze_mask_0, x = qkv_35)[name = tensor("v_35")]; + tensor freqs_35 = const()[name = tensor("freqs_35"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210644288)))]; + tensor var_7467 = const()[name = tensor("op_7467"), val = tensor([1, 1, 1, 1])]; + tensor ts_107 = reshape(shape = var_7467, x = position17)[name = tensor("ts_107")]; + tensor var_7471 = const()[name = tensor("op_7471"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_35 = reshape(shape = var_7471, x = q_103)[name = tensor("q_complex_35")]; + tensor var_7475 = const()[name = tensor("op_7475"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_35 = reshape(shape = var_7475, x = k_69)[name = tensor("k_complex_35")]; + tensor var_7479_begin_0 = const()[name = tensor("op_7479_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7479_end_0 = const()[name = tensor("op_7479_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7479_end_mask_0 = const()[name = tensor("op_7479_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7479_squeeze_mask_0 = const()[name = tensor("op_7479_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7479 = slice_by_index(begin = var_7479_begin_0, end = var_7479_end_0, end_mask = var_7479_end_mask_0, squeeze_mask = var_7479_squeeze_mask_0, x = q_complex_35)[name = tensor("op_7479")]; + tensor var_7487_begin_0 = const()[name = tensor("op_7487_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7487_end_0 = const()[name = tensor("op_7487_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7487_end_mask_0 = const()[name = tensor("op_7487_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7487_squeeze_mask_0 = const()[name = tensor("op_7487_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7487 = slice_by_index(begin = var_7487_begin_0, end = var_7487_end_0, end_mask = var_7487_end_mask_0, squeeze_mask = var_7487_squeeze_mask_0, x = q_complex_35)[name = tensor("op_7487")]; + tensor var_7495_begin_0 = const()[name = tensor("op_7495_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7495_end_0 = const()[name = tensor("op_7495_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7495_end_mask_0 = const()[name = tensor("op_7495_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7495_squeeze_mask_0 = const()[name = tensor("op_7495_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7495 = slice_by_index(begin = var_7495_begin_0, end = var_7495_end_0, end_mask = var_7495_end_mask_0, squeeze_mask = var_7495_squeeze_mask_0, x = k_complex_35)[name = tensor("op_7495")]; + tensor var_7503_begin_0 = const()[name = tensor("op_7503_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7503_end_0 = const()[name = tensor("op_7503_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7503_end_mask_0 = const()[name = tensor("op_7503_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7503_squeeze_mask_0 = const()[name = tensor("op_7503_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7503 = slice_by_index(begin = var_7503_begin_0, end = var_7503_end_0, end_mask = var_7503_end_mask_0, squeeze_mask = var_7503_squeeze_mask_0, x = k_complex_35)[name = tensor("op_7503")]; + tensor var_7509 = mul(x = freqs_35, y = ts_107)[name = tensor("op_7509")]; + tensor rotr_35 = cos(x = var_7509)[name = tensor("rotr_35")]; + tensor roti_35 = sin(x = var_7509)[name = tensor("roti_35")]; + tensor var_7513 = mul(x = var_7479, y = rotr_35)[name = tensor("op_7513")]; + tensor var_7514 = mul(x = var_7487, y = roti_35)[name = tensor("op_7514")]; + tensor qor_69 = sub(x = var_7513, y = var_7514)[name = tensor("qor_69")]; + tensor var_7517 = mul(x = var_7479, y = roti_35)[name = tensor("op_7517")]; + tensor var_7518 = mul(x = var_7487, y = rotr_35)[name = tensor("op_7518")]; + tensor qoi_69 = add(x = var_7517, y = var_7518)[name = tensor("qoi_69")]; + tensor var_7521 = mul(x = var_7495, y = rotr_35)[name = tensor("op_7521")]; + tensor var_7522 = mul(x = var_7503, y = roti_35)[name = tensor("op_7522")]; + tensor kor_69 = sub(x = var_7521, y = var_7522)[name = tensor("kor_69")]; + tensor var_7525 = mul(x = var_7495, y = roti_35)[name = tensor("op_7525")]; + tensor var_7526 = mul(x = var_7503, y = rotr_35)[name = tensor("op_7526")]; + tensor koi_69 = add(x = var_7525, y = var_7526)[name = tensor("koi_69")]; + tensor qo_35_axis_0 = const()[name = tensor("qo_35_axis_0"), val = tensor(-1)]; + tensor qo_35 = stack(axis = qo_35_axis_0, values = (qor_69, qoi_69))[name = tensor("qo_35")]; + tensor ko_35_axis_0 = const()[name = tensor("ko_35_axis_0"), val = tensor(-1)]; + tensor ko_35 = stack(axis = ko_35_axis_0, values = (kor_69, koi_69))[name = tensor("ko_35")]; + tensor var_7555 = const()[name = tensor("op_7555"), val = tensor([1, 1, 16, 64])]; + tensor q_105 = reshape(shape = var_7555, x = qo_35)[name = tensor("q_105")]; + tensor var_7557 = const()[name = tensor("op_7557"), val = tensor([1, 1, 16, 64])]; + tensor k_71 = reshape(shape = var_7557, x = ko_35)[name = tensor("k_71")]; + tensor _inversed_7579_y_0 = const()[name = tensor("_inversed_7579_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_7579 = mul(x = ts_107, y = _inversed_7579_y_0)[name = tensor("_inversed_7579")]; + tensor var_7580 = floor(x = _inversed_7579)[name = tensor("op_7580")]; + tensor var_7581 = const()[name = tensor("op_7581"), val = tensor(0x1p+9)]; + tensor var_7582 = mul(x = var_7580, y = var_7581)[name = tensor("op_7582")]; + tensor write_indices_float_71 = sub(x = ts_107, y = var_7582)[name = tensor("write_indices_float_71")]; + tensor var_7589_dtype_0 = const()[name = tensor("op_7589_dtype_0"), val = tensor("int32")]; + tensor write_indices_35_reps_0 = const()[name = tensor("write_indices_35_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_7589 = cast(dtype = var_7589_dtype_0, x = write_indices_float_71)[name = tensor("cast_438")]; + tensor write_indices_35 = tile(reps = write_indices_35_reps_0, x = var_7589)[name = tensor("write_indices_35")]; + tensor var_7597_begin_0 = const()[name = tensor("op_7597_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7597_end_0 = const()[name = tensor("op_7597_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_7597_end_mask_0 = const()[name = tensor("op_7597_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7597_squeeze_mask_0 = const()[name = tensor("op_7597_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7597 = slice_by_index(begin = var_7597_begin_0, end = var_7597_end_0, end_mask = var_7597_end_mask_0, squeeze_mask = var_7597_squeeze_mask_0, x = cache17)[name = tensor("op_7597")]; + tensor var_7599_axis_0 = const()[name = tensor("op_7599_axis_0"), val = tensor(1)]; + tensor var_7599_mode_0 = const()[name = tensor("op_7599_mode_0"), val = tensor("update")]; + tensor var_7599_validate_indices_0 = const()[name = tensor("op_7599_validate_indices_0"), val = tensor(false)]; + tensor var_7599 = scatter_along_axis(axis = var_7599_axis_0, data = var_7597, indices = write_indices_35, mode = var_7599_mode_0, updates = k_71, validate_indices = var_7599_validate_indices_0)[name = tensor("op_7599")]; + tensor concat_121 = const()[name = tensor("concat_121"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_122 = const()[name = tensor("concat_122"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_35_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_35_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_82 = const()[name = tensor("shape_82"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_34 = const()[name = tensor("reduce_prod_34"), val = tensor(1048576)]; + tensor range_1d_34_start_0 = const()[name = tensor("range_1d_34_start_0"), val = tensor(0)]; + tensor range_1d_34_step_0 = const()[name = tensor("range_1d_34_step_0"), val = tensor(1)]; + tensor range_1d_34 = range_1d(end = reduce_prod_34, start = range_1d_34_start_0, step = range_1d_34_step_0)[name = tensor("range_1d_34")]; + tensor reshape_170 = reshape(shape = shape_82, x = range_1d_34)[name = tensor("reshape_170")]; + tensor slice_by_index_34 = slice_by_index(begin = concat_121, begin_mask = new_cache_35_internal_tensor_assign_1_begin_mask_0, end = concat_122, end_mask = new_cache_35_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_35_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_35_internal_tensor_assign_1_stride_0, x = reshape_170)[name = tensor("slice_by_index_34")]; + tensor reshape_171_shape_0 = const()[name = tensor("reshape_171_shape_0"), val = tensor([-1])]; + tensor reshape_171 = reshape(shape = reshape_171_shape_0, x = slice_by_index_34)[name = tensor("reshape_171")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([-1])]; + tensor reshape_172 = reshape(shape = reshape_172_shape_0, x = var_7599)[name = tensor("reshape_172")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([-1])]; + tensor reshape_173 = reshape(shape = reshape_173_shape_0, x = cache17)[name = tensor("reshape_173")]; + tensor scatter_34_mode_0 = const()[name = tensor("scatter_34_mode_0"), val = tensor("update")]; + tensor scatter_34_axis_0 = const()[name = tensor("scatter_34_axis_0"), val = tensor(0)]; + tensor scatter_34_validate_indices_0 = const()[name = tensor("scatter_34_validate_indices_0"), val = tensor(false)]; + tensor scatter_34 = scatter(axis = scatter_34_axis_0, data = reshape_173, indices = reshape_171, mode = scatter_34_mode_0, updates = reshape_172, validate_indices = scatter_34_validate_indices_0)[name = tensor("scatter_34")]; + tensor reshape_174 = reshape(shape = shape_82, x = scatter_34)[name = tensor("reshape_174")]; + tensor var_7607_begin_0 = const()[name = tensor("op_7607_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_7607_end_0 = const()[name = tensor("op_7607_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_7607_end_mask_0 = const()[name = tensor("op_7607_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_7607_squeeze_mask_0 = const()[name = tensor("op_7607_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_7607 = slice_by_index(begin = var_7607_begin_0, end = var_7607_end_0, end_mask = var_7607_end_mask_0, squeeze_mask = var_7607_squeeze_mask_0, x = reshape_174)[name = tensor("op_7607")]; + tensor var_7609_axis_0 = const()[name = tensor("op_7609_axis_0"), val = tensor(1)]; + tensor var_7609_mode_0 = const()[name = tensor("op_7609_mode_0"), val = tensor("update")]; + tensor var_7609_validate_indices_0 = const()[name = tensor("op_7609_validate_indices_0"), val = tensor(false)]; + tensor var_7609 = scatter_along_axis(axis = var_7609_axis_0, data = var_7607, indices = write_indices_35, mode = var_7609_mode_0, updates = v_35, validate_indices = var_7609_validate_indices_0)[name = tensor("op_7609")]; + tensor concat_123 = const()[name = tensor("concat_123"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_124 = const()[name = tensor("concat_124"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_35_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_35_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_35_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_35_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_83 = const()[name = tensor("shape_83"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_35 = const()[name = tensor("reduce_prod_35"), val = tensor(1048576)]; + tensor range_1d_35_start_0 = const()[name = tensor("range_1d_35_start_0"), val = tensor(0)]; + tensor range_1d_35_step_0 = const()[name = tensor("range_1d_35_step_0"), val = tensor(1)]; + tensor range_1d_35 = range_1d(end = reduce_prod_35, start = range_1d_35_start_0, step = range_1d_35_step_0)[name = tensor("range_1d_35")]; + tensor reshape_175 = reshape(shape = shape_83, x = range_1d_35)[name = tensor("reshape_175")]; + tensor slice_by_index_35 = slice_by_index(begin = concat_123, begin_mask = new_cache_35_internal_tensor_assign_2_begin_mask_0, end = concat_124, end_mask = new_cache_35_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_35_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_35_internal_tensor_assign_2_stride_0, x = reshape_175)[name = tensor("slice_by_index_35")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([-1])]; + tensor reshape_176 = reshape(shape = reshape_176_shape_0, x = slice_by_index_35)[name = tensor("reshape_176")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([-1])]; + tensor reshape_177 = reshape(shape = reshape_177_shape_0, x = var_7609)[name = tensor("reshape_177")]; + tensor reshape_178_shape_0 = const()[name = tensor("reshape_178_shape_0"), val = tensor([-1])]; + tensor reshape_178 = reshape(shape = reshape_178_shape_0, x = reshape_174)[name = tensor("reshape_178")]; + tensor scatter_35_mode_0 = const()[name = tensor("scatter_35_mode_0"), val = tensor("update")]; + tensor scatter_35_axis_0 = const()[name = tensor("scatter_35_axis_0"), val = tensor(0)]; + tensor scatter_35_validate_indices_0 = const()[name = tensor("scatter_35_validate_indices_0"), val = tensor(false)]; + tensor scatter_35 = scatter(axis = scatter_35_axis_0, data = reshape_178, indices = reshape_176, mode = scatter_35_mode_0, updates = reshape_177, validate_indices = scatter_35_validate_indices_0)[name = tensor("scatter_35")]; + tensor new_cache_35_internal_tensor_assign_2 = reshape(shape = shape_83, x = scatter_35)[name = tensor("reshape_179")]; + tensor keys_103_begin_0 = const()[name = tensor("keys_103_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_103_end_0 = const()[name = tensor("keys_103_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_103_end_mask_0 = const()[name = tensor("keys_103_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_103_squeeze_mask_0 = const()[name = tensor("keys_103_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_103 = slice_by_index(begin = keys_103_begin_0, end = keys_103_end_0, end_mask = keys_103_end_mask_0, squeeze_mask = keys_103_squeeze_mask_0, x = new_cache_35_internal_tensor_assign_2)[name = tensor("keys_103")]; + tensor values_103_begin_0 = const()[name = tensor("values_103_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_103_end_0 = const()[name = tensor("values_103_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_103_end_mask_0 = const()[name = tensor("values_103_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_103_squeeze_mask_0 = const()[name = tensor("values_103_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_103 = slice_by_index(begin = values_103_begin_0, end = values_103_end_0, end_mask = values_103_end_mask_0, squeeze_mask = values_103_squeeze_mask_0, x = new_cache_35_internal_tensor_assign_2)[name = tensor("values_103")]; + tensor var_7621 = not_equal(x = keys_103, y = keys_103)[name = tensor("op_7621")]; + tensor keys_105 = select(a = var_504, b = keys_103, cond = var_7621)[name = tensor("keys_105")]; + tensor var_7629 = not_equal(x = values_103, y = values_103)[name = tensor("op_7629")]; + tensor values_105 = select(a = var_504, b = values_103, cond = var_7629)[name = tensor("values_105")]; + tensor var_7653 = const()[name = tensor("op_7653"), val = tensor([0, 2, 1, 3])]; + tensor var_7666 = const()[name = tensor("op_7666"), val = tensor([1, 1, 1])]; + tensor var_7667 = reshape(shape = var_7666, x = position17)[name = tensor("op_7667")]; + tensor var_7684 = const()[name = tensor("op_7684"), val = tensor(0x1p+0)]; + tensor valid_len_35 = add(x = var_7667, y = var_7684)[name = tensor("valid_len_35")]; + tensor valid_mask_35 = less(x = k_positions_1_promoted, y = valid_len_35)[name = tensor("valid_mask_35")]; + tensor causal_mask_35 = less_equal(x = k_positions_1_promoted, y = var_7667)[name = tensor("causal_mask_35")]; + tensor attn_mask_69 = logical_and(x = valid_mask_35, y = causal_mask_35)[name = tensor("attn_mask_69")]; + tensor attn_mask_71_axes_0 = const()[name = tensor("attn_mask_71_axes_0"), val = tensor([1])]; + tensor attn_mask_71 = expand_dims(axes = attn_mask_71_axes_0, x = attn_mask_69)[name = tensor("attn_mask_71")]; + tensor var_7696 = const()[name = tensor("op_7696"), val = tensor([0x1.fffe5cp-4])]; + tensor var_7702_transpose_x_0 = const()[name = tensor("op_7702_transpose_x_0"), val = tensor(false)]; + tensor var_7702_transpose_y_0 = const()[name = tensor("op_7702_transpose_y_0"), val = tensor(false)]; + tensor transpose_106_perm_0 = const()[name = tensor("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = tensor("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = keys_105)[name = tensor("transpose_145")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = q_105)[name = tensor("transpose_146")]; + tensor var_7702 = matmul(transpose_x = var_7702_transpose_x_0, transpose_y = var_7702_transpose_y_0, x = transpose_106, y = transpose_107)[name = tensor("op_7702")]; + tensor attn_weights_103 = mul(x = var_7702, y = var_7696)[name = tensor("attn_weights_103")]; + tensor var_7704 = logical_not(x = attn_mask_71)[name = tensor("op_7704")]; + tensor var_7705 = const()[name = tensor("op_7705"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_105 = select(a = var_7705, b = attn_weights_103, cond = var_7704)[name = tensor("attn_weights_105")]; + tensor var_7707 = const()[name = tensor("op_7707"), val = tensor(-1)]; + tensor attn_weights_107 = softmax(axis = var_7707, x = attn_weights_105)[name = tensor("attn_weights_107")]; + tensor attn_output_35_transpose_x_0 = const()[name = tensor("attn_output_35_transpose_x_0"), val = tensor(false)]; + tensor attn_output_35_transpose_y_0 = const()[name = tensor("attn_output_35_transpose_y_0"), val = tensor(false)]; + tensor values_107 = transpose(perm = var_7653, x = values_105)[name = tensor("transpose_147")]; + tensor attn_output_35 = matmul(transpose_x = attn_output_35_transpose_x_0, transpose_y = attn_output_35_transpose_y_0, x = attn_weights_107, y = values_107)[name = tensor("attn_output_35")]; + tensor var_7715 = const()[name = tensor("op_7715"), val = tensor([0, 2, 1, 3])]; + tensor var_7718 = const()[name = tensor("op_7718"), val = tensor([1, 1, 1024])]; + tensor var_7716 = transpose(perm = var_7715, x = attn_output_35)[name = tensor("transpose_144")]; + tensor input_175 = reshape(shape = var_7718, x = var_7716)[name = tensor("input_175")]; + tensor attn_out_35 = linear(bias = linear_0_bias_0, weight = attn17_out_proj_weight, x = input_175)[name = tensor("linear_70")]; + tensor var_7724 = const()[name = tensor("op_7724"), val = tensor(0x1p+0)]; + tensor var_7725 = add(x = position17, y = var_7724)[name = tensor("op_7725")]; + tensor input_177 = add(x = input_173, y = attn_out_35)[name = tensor("input_177")]; + tensor var_7729 = const()[name = tensor("op_7729"), val = tensor(0x1.4f8b58p-17)]; + tensor input_179_axes_0 = const()[name = tensor("input_179_axes_0"), val = tensor([-1])]; + tensor input_179 = layer_norm(axes = input_179_axes_0, beta = norm17_2_bias, epsilon = var_7729, gamma = norm17_2_weight, x = input_177)[name = tensor("input_179")]; + tensor var_7737 = linear(bias = linear_3_bias_0, weight = linear17_1_weight, x = input_179)[name = tensor("linear_71")]; + tensor input_181_mode_0 = const()[name = tensor("input_181_mode_0"), val = tensor("EXACT")]; + tensor input_181 = gelu(mode = input_181_mode_0, x = var_7737)[name = tensor("input_181")]; + tensor ffn_out_35 = linear(bias = linear_0_bias_0, weight = linear17_2_weight, x = input_181)[name = tensor("linear_72")]; + tensor input_183 = add(x = input_177, y = ffn_out_35)[name = tensor("input_183")]; + tensor var_7746 = const()[name = tensor("op_7746"), val = tensor(0x1.4f8b58p-17)]; + tensor x_37_axes_0 = const()[name = tensor("x_37_axes_0"), val = tensor([-1])]; + tensor x_37 = layer_norm(axes = x_37_axes_0, beta = norm18_1_bias, epsilon = var_7746, gamma = norm18_1_weight, x = input_183)[name = tensor("x_37")]; + tensor var_7778 = linear(bias = linear_1_bias_0, weight = attn18_in_proj_weight, x = x_37)[name = tensor("linear_73")]; + tensor var_7782 = const()[name = tensor("op_7782"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_37 = reshape(shape = var_7782, x = var_7778)[name = tensor("qkv_37")]; + tensor q_109_begin_0 = const()[name = tensor("q_109_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_109_end_0 = const()[name = tensor("q_109_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_109_end_mask_0 = const()[name = tensor("q_109_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_109_squeeze_mask_0 = const()[name = tensor("q_109_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_109 = slice_by_index(begin = q_109_begin_0, end = q_109_end_0, end_mask = q_109_end_mask_0, squeeze_mask = q_109_squeeze_mask_0, x = qkv_37)[name = tensor("q_109")]; + tensor k_73_begin_0 = const()[name = tensor("k_73_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_73_end_0 = const()[name = tensor("k_73_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_73_end_mask_0 = const()[name = tensor("k_73_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_73_squeeze_mask_0 = const()[name = tensor("k_73_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_73 = slice_by_index(begin = k_73_begin_0, end = k_73_end_0, end_mask = k_73_end_mask_0, squeeze_mask = k_73_squeeze_mask_0, x = qkv_37)[name = tensor("k_73")]; + tensor v_37_begin_0 = const()[name = tensor("v_37_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_37_end_0 = const()[name = tensor("v_37_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_37_end_mask_0 = const()[name = tensor("v_37_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_37_squeeze_mask_0 = const()[name = tensor("v_37_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_37 = slice_by_index(begin = v_37_begin_0, end = v_37_end_0, end_mask = v_37_end_mask_0, squeeze_mask = v_37_squeeze_mask_0, x = qkv_37)[name = tensor("v_37")]; + tensor freqs_37 = const()[name = tensor("freqs_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210644480)))]; + tensor var_7886 = const()[name = tensor("op_7886"), val = tensor([1, 1, 1, 1])]; + tensor ts_113 = reshape(shape = var_7886, x = position18)[name = tensor("ts_113")]; + tensor var_7890 = const()[name = tensor("op_7890"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_37 = reshape(shape = var_7890, x = q_109)[name = tensor("q_complex_37")]; + tensor var_7894 = const()[name = tensor("op_7894"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_37 = reshape(shape = var_7894, x = k_73)[name = tensor("k_complex_37")]; + tensor var_7898_begin_0 = const()[name = tensor("op_7898_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7898_end_0 = const()[name = tensor("op_7898_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7898_end_mask_0 = const()[name = tensor("op_7898_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7898_squeeze_mask_0 = const()[name = tensor("op_7898_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7898 = slice_by_index(begin = var_7898_begin_0, end = var_7898_end_0, end_mask = var_7898_end_mask_0, squeeze_mask = var_7898_squeeze_mask_0, x = q_complex_37)[name = tensor("op_7898")]; + tensor var_7906_begin_0 = const()[name = tensor("op_7906_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7906_end_0 = const()[name = tensor("op_7906_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7906_end_mask_0 = const()[name = tensor("op_7906_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7906_squeeze_mask_0 = const()[name = tensor("op_7906_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7906 = slice_by_index(begin = var_7906_begin_0, end = var_7906_end_0, end_mask = var_7906_end_mask_0, squeeze_mask = var_7906_squeeze_mask_0, x = q_complex_37)[name = tensor("op_7906")]; + tensor var_7914_begin_0 = const()[name = tensor("op_7914_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_7914_end_0 = const()[name = tensor("op_7914_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_7914_end_mask_0 = const()[name = tensor("op_7914_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7914_squeeze_mask_0 = const()[name = tensor("op_7914_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7914 = slice_by_index(begin = var_7914_begin_0, end = var_7914_end_0, end_mask = var_7914_end_mask_0, squeeze_mask = var_7914_squeeze_mask_0, x = k_complex_37)[name = tensor("op_7914")]; + tensor var_7922_begin_0 = const()[name = tensor("op_7922_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_7922_end_0 = const()[name = tensor("op_7922_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_7922_end_mask_0 = const()[name = tensor("op_7922_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_7922_squeeze_mask_0 = const()[name = tensor("op_7922_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_7922 = slice_by_index(begin = var_7922_begin_0, end = var_7922_end_0, end_mask = var_7922_end_mask_0, squeeze_mask = var_7922_squeeze_mask_0, x = k_complex_37)[name = tensor("op_7922")]; + tensor var_7928 = mul(x = freqs_37, y = ts_113)[name = tensor("op_7928")]; + tensor rotr_37 = cos(x = var_7928)[name = tensor("rotr_37")]; + tensor roti_37 = sin(x = var_7928)[name = tensor("roti_37")]; + tensor var_7932 = mul(x = var_7898, y = rotr_37)[name = tensor("op_7932")]; + tensor var_7933 = mul(x = var_7906, y = roti_37)[name = tensor("op_7933")]; + tensor qor_73 = sub(x = var_7932, y = var_7933)[name = tensor("qor_73")]; + tensor var_7936 = mul(x = var_7898, y = roti_37)[name = tensor("op_7936")]; + tensor var_7937 = mul(x = var_7906, y = rotr_37)[name = tensor("op_7937")]; + tensor qoi_73 = add(x = var_7936, y = var_7937)[name = tensor("qoi_73")]; + tensor var_7940 = mul(x = var_7914, y = rotr_37)[name = tensor("op_7940")]; + tensor var_7941 = mul(x = var_7922, y = roti_37)[name = tensor("op_7941")]; + tensor kor_73 = sub(x = var_7940, y = var_7941)[name = tensor("kor_73")]; + tensor var_7944 = mul(x = var_7914, y = roti_37)[name = tensor("op_7944")]; + tensor var_7945 = mul(x = var_7922, y = rotr_37)[name = tensor("op_7945")]; + tensor koi_73 = add(x = var_7944, y = var_7945)[name = tensor("koi_73")]; + tensor qo_37_axis_0 = const()[name = tensor("qo_37_axis_0"), val = tensor(-1)]; + tensor qo_37 = stack(axis = qo_37_axis_0, values = (qor_73, qoi_73))[name = tensor("qo_37")]; + tensor ko_37_axis_0 = const()[name = tensor("ko_37_axis_0"), val = tensor(-1)]; + tensor ko_37 = stack(axis = ko_37_axis_0, values = (kor_73, koi_73))[name = tensor("ko_37")]; + tensor var_7974 = const()[name = tensor("op_7974"), val = tensor([1, 1, 16, 64])]; + tensor q_111 = reshape(shape = var_7974, x = qo_37)[name = tensor("q_111")]; + tensor var_7976 = const()[name = tensor("op_7976"), val = tensor([1, 1, 16, 64])]; + tensor k_75 = reshape(shape = var_7976, x = ko_37)[name = tensor("k_75")]; + tensor _inversed_7998_y_0 = const()[name = tensor("_inversed_7998_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_7998 = mul(x = ts_113, y = _inversed_7998_y_0)[name = tensor("_inversed_7998")]; + tensor var_7999 = floor(x = _inversed_7998)[name = tensor("op_7999")]; + tensor var_8000 = const()[name = tensor("op_8000"), val = tensor(0x1p+9)]; + tensor var_8001 = mul(x = var_7999, y = var_8000)[name = tensor("op_8001")]; + tensor write_indices_float_75 = sub(x = ts_113, y = var_8001)[name = tensor("write_indices_float_75")]; + tensor var_8008_dtype_0 = const()[name = tensor("op_8008_dtype_0"), val = tensor("int32")]; + tensor write_indices_37_reps_0 = const()[name = tensor("write_indices_37_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_8008 = cast(dtype = var_8008_dtype_0, x = write_indices_float_75)[name = tensor("cast_437")]; + tensor write_indices_37 = tile(reps = write_indices_37_reps_0, x = var_8008)[name = tensor("write_indices_37")]; + tensor var_8016_begin_0 = const()[name = tensor("op_8016_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8016_end_0 = const()[name = tensor("op_8016_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_8016_end_mask_0 = const()[name = tensor("op_8016_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8016_squeeze_mask_0 = const()[name = tensor("op_8016_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8016 = slice_by_index(begin = var_8016_begin_0, end = var_8016_end_0, end_mask = var_8016_end_mask_0, squeeze_mask = var_8016_squeeze_mask_0, x = cache18)[name = tensor("op_8016")]; + tensor var_8018_axis_0 = const()[name = tensor("op_8018_axis_0"), val = tensor(1)]; + tensor var_8018_mode_0 = const()[name = tensor("op_8018_mode_0"), val = tensor("update")]; + tensor var_8018_validate_indices_0 = const()[name = tensor("op_8018_validate_indices_0"), val = tensor(false)]; + tensor var_8018 = scatter_along_axis(axis = var_8018_axis_0, data = var_8016, indices = write_indices_37, mode = var_8018_mode_0, updates = k_75, validate_indices = var_8018_validate_indices_0)[name = tensor("op_8018")]; + tensor concat_128 = const()[name = tensor("concat_128"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_129 = const()[name = tensor("concat_129"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_37_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_37_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_84 = const()[name = tensor("shape_84"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_36 = const()[name = tensor("reduce_prod_36"), val = tensor(1048576)]; + tensor range_1d_36_start_0 = const()[name = tensor("range_1d_36_start_0"), val = tensor(0)]; + tensor range_1d_36_step_0 = const()[name = tensor("range_1d_36_step_0"), val = tensor(1)]; + tensor range_1d_36 = range_1d(end = reduce_prod_36, start = range_1d_36_start_0, step = range_1d_36_step_0)[name = tensor("range_1d_36")]; + tensor reshape_180 = reshape(shape = shape_84, x = range_1d_36)[name = tensor("reshape_180")]; + tensor slice_by_index_36 = slice_by_index(begin = concat_128, begin_mask = new_cache_37_internal_tensor_assign_1_begin_mask_0, end = concat_129, end_mask = new_cache_37_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_37_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_37_internal_tensor_assign_1_stride_0, x = reshape_180)[name = tensor("slice_by_index_36")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([-1])]; + tensor reshape_181 = reshape(shape = reshape_181_shape_0, x = slice_by_index_36)[name = tensor("reshape_181")]; + tensor reshape_182_shape_0 = const()[name = tensor("reshape_182_shape_0"), val = tensor([-1])]; + tensor reshape_182 = reshape(shape = reshape_182_shape_0, x = var_8018)[name = tensor("reshape_182")]; + tensor reshape_183_shape_0 = const()[name = tensor("reshape_183_shape_0"), val = tensor([-1])]; + tensor reshape_183 = reshape(shape = reshape_183_shape_0, x = cache18)[name = tensor("reshape_183")]; + tensor scatter_36_mode_0 = const()[name = tensor("scatter_36_mode_0"), val = tensor("update")]; + tensor scatter_36_axis_0 = const()[name = tensor("scatter_36_axis_0"), val = tensor(0)]; + tensor scatter_36_validate_indices_0 = const()[name = tensor("scatter_36_validate_indices_0"), val = tensor(false)]; + tensor scatter_36 = scatter(axis = scatter_36_axis_0, data = reshape_183, indices = reshape_181, mode = scatter_36_mode_0, updates = reshape_182, validate_indices = scatter_36_validate_indices_0)[name = tensor("scatter_36")]; + tensor reshape_184 = reshape(shape = shape_84, x = scatter_36)[name = tensor("reshape_184")]; + tensor var_8026_begin_0 = const()[name = tensor("op_8026_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_8026_end_0 = const()[name = tensor("op_8026_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_8026_end_mask_0 = const()[name = tensor("op_8026_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8026_squeeze_mask_0 = const()[name = tensor("op_8026_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8026 = slice_by_index(begin = var_8026_begin_0, end = var_8026_end_0, end_mask = var_8026_end_mask_0, squeeze_mask = var_8026_squeeze_mask_0, x = reshape_184)[name = tensor("op_8026")]; + tensor var_8028_axis_0 = const()[name = tensor("op_8028_axis_0"), val = tensor(1)]; + tensor var_8028_mode_0 = const()[name = tensor("op_8028_mode_0"), val = tensor("update")]; + tensor var_8028_validate_indices_0 = const()[name = tensor("op_8028_validate_indices_0"), val = tensor(false)]; + tensor var_8028 = scatter_along_axis(axis = var_8028_axis_0, data = var_8026, indices = write_indices_37, mode = var_8028_mode_0, updates = v_37, validate_indices = var_8028_validate_indices_0)[name = tensor("op_8028")]; + tensor concat_130 = const()[name = tensor("concat_130"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_131 = const()[name = tensor("concat_131"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_37_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_37_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_37_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_37_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_85 = const()[name = tensor("shape_85"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_37 = const()[name = tensor("reduce_prod_37"), val = tensor(1048576)]; + tensor range_1d_37_start_0 = const()[name = tensor("range_1d_37_start_0"), val = tensor(0)]; + tensor range_1d_37_step_0 = const()[name = tensor("range_1d_37_step_0"), val = tensor(1)]; + tensor range_1d_37 = range_1d(end = reduce_prod_37, start = range_1d_37_start_0, step = range_1d_37_step_0)[name = tensor("range_1d_37")]; + tensor reshape_185 = reshape(shape = shape_85, x = range_1d_37)[name = tensor("reshape_185")]; + tensor slice_by_index_37 = slice_by_index(begin = concat_130, begin_mask = new_cache_37_internal_tensor_assign_2_begin_mask_0, end = concat_131, end_mask = new_cache_37_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_37_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_37_internal_tensor_assign_2_stride_0, x = reshape_185)[name = tensor("slice_by_index_37")]; + tensor reshape_186_shape_0 = const()[name = tensor("reshape_186_shape_0"), val = tensor([-1])]; + tensor reshape_186 = reshape(shape = reshape_186_shape_0, x = slice_by_index_37)[name = tensor("reshape_186")]; + tensor reshape_187_shape_0 = const()[name = tensor("reshape_187_shape_0"), val = tensor([-1])]; + tensor reshape_187 = reshape(shape = reshape_187_shape_0, x = var_8028)[name = tensor("reshape_187")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([-1])]; + tensor reshape_188 = reshape(shape = reshape_188_shape_0, x = reshape_184)[name = tensor("reshape_188")]; + tensor scatter_37_mode_0 = const()[name = tensor("scatter_37_mode_0"), val = tensor("update")]; + tensor scatter_37_axis_0 = const()[name = tensor("scatter_37_axis_0"), val = tensor(0)]; + tensor scatter_37_validate_indices_0 = const()[name = tensor("scatter_37_validate_indices_0"), val = tensor(false)]; + tensor scatter_37 = scatter(axis = scatter_37_axis_0, data = reshape_188, indices = reshape_186, mode = scatter_37_mode_0, updates = reshape_187, validate_indices = scatter_37_validate_indices_0)[name = tensor("scatter_37")]; + tensor new_cache_37_internal_tensor_assign_2 = reshape(shape = shape_85, x = scatter_37)[name = tensor("reshape_189")]; + tensor keys_109_begin_0 = const()[name = tensor("keys_109_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_109_end_0 = const()[name = tensor("keys_109_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_109_end_mask_0 = const()[name = tensor("keys_109_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_109_squeeze_mask_0 = const()[name = tensor("keys_109_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_109 = slice_by_index(begin = keys_109_begin_0, end = keys_109_end_0, end_mask = keys_109_end_mask_0, squeeze_mask = keys_109_squeeze_mask_0, x = new_cache_37_internal_tensor_assign_2)[name = tensor("keys_109")]; + tensor values_109_begin_0 = const()[name = tensor("values_109_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_109_end_0 = const()[name = tensor("values_109_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_109_end_mask_0 = const()[name = tensor("values_109_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_109_squeeze_mask_0 = const()[name = tensor("values_109_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_109 = slice_by_index(begin = values_109_begin_0, end = values_109_end_0, end_mask = values_109_end_mask_0, squeeze_mask = values_109_squeeze_mask_0, x = new_cache_37_internal_tensor_assign_2)[name = tensor("values_109")]; + tensor var_8040 = not_equal(x = keys_109, y = keys_109)[name = tensor("op_8040")]; + tensor keys_111 = select(a = var_504, b = keys_109, cond = var_8040)[name = tensor("keys_111")]; + tensor var_8048 = not_equal(x = values_109, y = values_109)[name = tensor("op_8048")]; + tensor values_111 = select(a = var_504, b = values_109, cond = var_8048)[name = tensor("values_111")]; + tensor var_8072 = const()[name = tensor("op_8072"), val = tensor([0, 2, 1, 3])]; + tensor var_8085 = const()[name = tensor("op_8085"), val = tensor([1, 1, 1])]; + tensor var_8086 = reshape(shape = var_8085, x = position18)[name = tensor("op_8086")]; + tensor var_8103 = const()[name = tensor("op_8103"), val = tensor(0x1p+0)]; + tensor valid_len_37 = add(x = var_8086, y = var_8103)[name = tensor("valid_len_37")]; + tensor valid_mask_37 = less(x = k_positions_1_promoted, y = valid_len_37)[name = tensor("valid_mask_37")]; + tensor causal_mask_37 = less_equal(x = k_positions_1_promoted, y = var_8086)[name = tensor("causal_mask_37")]; + tensor attn_mask_73 = logical_and(x = valid_mask_37, y = causal_mask_37)[name = tensor("attn_mask_73")]; + tensor attn_mask_75_axes_0 = const()[name = tensor("attn_mask_75_axes_0"), val = tensor([1])]; + tensor attn_mask_75 = expand_dims(axes = attn_mask_75_axes_0, x = attn_mask_73)[name = tensor("attn_mask_75")]; + tensor var_8115 = const()[name = tensor("op_8115"), val = tensor([0x1.fffe5cp-4])]; + tensor var_8121_transpose_x_0 = const()[name = tensor("op_8121_transpose_x_0"), val = tensor(false)]; + tensor var_8121_transpose_y_0 = const()[name = tensor("op_8121_transpose_y_0"), val = tensor(false)]; + tensor transpose_108_perm_0 = const()[name = tensor("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = tensor("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = keys_111)[name = tensor("transpose_141")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = q_111)[name = tensor("transpose_142")]; + tensor var_8121 = matmul(transpose_x = var_8121_transpose_x_0, transpose_y = var_8121_transpose_y_0, x = transpose_108, y = transpose_109)[name = tensor("op_8121")]; + tensor attn_weights_109 = mul(x = var_8121, y = var_8115)[name = tensor("attn_weights_109")]; + tensor var_8123 = logical_not(x = attn_mask_75)[name = tensor("op_8123")]; + tensor var_8124 = const()[name = tensor("op_8124"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_111 = select(a = var_8124, b = attn_weights_109, cond = var_8123)[name = tensor("attn_weights_111")]; + tensor var_8126 = const()[name = tensor("op_8126"), val = tensor(-1)]; + tensor attn_weights_113 = softmax(axis = var_8126, x = attn_weights_111)[name = tensor("attn_weights_113")]; + tensor attn_output_37_transpose_x_0 = const()[name = tensor("attn_output_37_transpose_x_0"), val = tensor(false)]; + tensor attn_output_37_transpose_y_0 = const()[name = tensor("attn_output_37_transpose_y_0"), val = tensor(false)]; + tensor values_113 = transpose(perm = var_8072, x = values_111)[name = tensor("transpose_143")]; + tensor attn_output_37 = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = attn_weights_113, y = values_113)[name = tensor("attn_output_37")]; + tensor var_8134 = const()[name = tensor("op_8134"), val = tensor([0, 2, 1, 3])]; + tensor var_8137 = const()[name = tensor("op_8137"), val = tensor([1, 1, 1024])]; + tensor var_8135 = transpose(perm = var_8134, x = attn_output_37)[name = tensor("transpose_140")]; + tensor input_185 = reshape(shape = var_8137, x = var_8135)[name = tensor("input_185")]; + tensor attn_out_37 = linear(bias = linear_0_bias_0, weight = attn18_out_proj_weight, x = input_185)[name = tensor("linear_74")]; + tensor var_8143 = const()[name = tensor("op_8143"), val = tensor(0x1p+0)]; + tensor var_8144 = add(x = position18, y = var_8143)[name = tensor("op_8144")]; + tensor input_187 = add(x = input_183, y = attn_out_37)[name = tensor("input_187")]; + tensor var_8148 = const()[name = tensor("op_8148"), val = tensor(0x1.4f8b58p-17)]; + tensor input_189_axes_0 = const()[name = tensor("input_189_axes_0"), val = tensor([-1])]; + tensor input_189 = layer_norm(axes = input_189_axes_0, beta = norm18_2_bias, epsilon = var_8148, gamma = norm18_2_weight, x = input_187)[name = tensor("input_189")]; + tensor var_8156 = linear(bias = linear_3_bias_0, weight = linear18_1_weight, x = input_189)[name = tensor("linear_75")]; + tensor input_191_mode_0 = const()[name = tensor("input_191_mode_0"), val = tensor("EXACT")]; + tensor input_191 = gelu(mode = input_191_mode_0, x = var_8156)[name = tensor("input_191")]; + tensor ffn_out_37 = linear(bias = linear_0_bias_0, weight = linear18_2_weight, x = input_191)[name = tensor("linear_76")]; + tensor input_193 = add(x = input_187, y = ffn_out_37)[name = tensor("input_193")]; + tensor var_8165 = const()[name = tensor("op_8165"), val = tensor(0x1.4f8b58p-17)]; + tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; + tensor x_39 = layer_norm(axes = x_39_axes_0, beta = norm19_1_bias, epsilon = var_8165, gamma = norm19_1_weight, x = input_193)[name = tensor("x_39")]; + tensor var_8197 = linear(bias = linear_1_bias_0, weight = attn19_in_proj_weight, x = x_39)[name = tensor("linear_77")]; + tensor var_8201 = const()[name = tensor("op_8201"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_39 = reshape(shape = var_8201, x = var_8197)[name = tensor("qkv_39")]; + tensor q_115_begin_0 = const()[name = tensor("q_115_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_115_end_0 = const()[name = tensor("q_115_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_115_end_mask_0 = const()[name = tensor("q_115_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_115_squeeze_mask_0 = const()[name = tensor("q_115_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_115 = slice_by_index(begin = q_115_begin_0, end = q_115_end_0, end_mask = q_115_end_mask_0, squeeze_mask = q_115_squeeze_mask_0, x = qkv_39)[name = tensor("q_115")]; + tensor k_77_begin_0 = const()[name = tensor("k_77_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_77_end_0 = const()[name = tensor("k_77_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_77_end_mask_0 = const()[name = tensor("k_77_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_77_squeeze_mask_0 = const()[name = tensor("k_77_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_77 = slice_by_index(begin = k_77_begin_0, end = k_77_end_0, end_mask = k_77_end_mask_0, squeeze_mask = k_77_squeeze_mask_0, x = qkv_39)[name = tensor("k_77")]; + tensor v_39_begin_0 = const()[name = tensor("v_39_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_39_end_0 = const()[name = tensor("v_39_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_39_end_mask_0 = const()[name = tensor("v_39_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_39_squeeze_mask_0 = const()[name = tensor("v_39_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_39 = slice_by_index(begin = v_39_begin_0, end = v_39_end_0, end_mask = v_39_end_mask_0, squeeze_mask = v_39_squeeze_mask_0, x = qkv_39)[name = tensor("v_39")]; + tensor freqs_39 = const()[name = tensor("freqs_39"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210644672)))]; + tensor var_8305 = const()[name = tensor("op_8305"), val = tensor([1, 1, 1, 1])]; + tensor ts_119 = reshape(shape = var_8305, x = position19)[name = tensor("ts_119")]; + tensor var_8309 = const()[name = tensor("op_8309"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_39 = reshape(shape = var_8309, x = q_115)[name = tensor("q_complex_39")]; + tensor var_8313 = const()[name = tensor("op_8313"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_39 = reshape(shape = var_8313, x = k_77)[name = tensor("k_complex_39")]; + tensor var_8317_begin_0 = const()[name = tensor("op_8317_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8317_end_0 = const()[name = tensor("op_8317_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8317_end_mask_0 = const()[name = tensor("op_8317_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8317_squeeze_mask_0 = const()[name = tensor("op_8317_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8317 = slice_by_index(begin = var_8317_begin_0, end = var_8317_end_0, end_mask = var_8317_end_mask_0, squeeze_mask = var_8317_squeeze_mask_0, x = q_complex_39)[name = tensor("op_8317")]; + tensor var_8325_begin_0 = const()[name = tensor("op_8325_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8325_end_0 = const()[name = tensor("op_8325_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8325_end_mask_0 = const()[name = tensor("op_8325_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8325_squeeze_mask_0 = const()[name = tensor("op_8325_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8325 = slice_by_index(begin = var_8325_begin_0, end = var_8325_end_0, end_mask = var_8325_end_mask_0, squeeze_mask = var_8325_squeeze_mask_0, x = q_complex_39)[name = tensor("op_8325")]; + tensor var_8333_begin_0 = const()[name = tensor("op_8333_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8333_end_0 = const()[name = tensor("op_8333_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8333_end_mask_0 = const()[name = tensor("op_8333_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8333_squeeze_mask_0 = const()[name = tensor("op_8333_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8333 = slice_by_index(begin = var_8333_begin_0, end = var_8333_end_0, end_mask = var_8333_end_mask_0, squeeze_mask = var_8333_squeeze_mask_0, x = k_complex_39)[name = tensor("op_8333")]; + tensor var_8341_begin_0 = const()[name = tensor("op_8341_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8341_end_0 = const()[name = tensor("op_8341_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8341_end_mask_0 = const()[name = tensor("op_8341_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8341_squeeze_mask_0 = const()[name = tensor("op_8341_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8341 = slice_by_index(begin = var_8341_begin_0, end = var_8341_end_0, end_mask = var_8341_end_mask_0, squeeze_mask = var_8341_squeeze_mask_0, x = k_complex_39)[name = tensor("op_8341")]; + tensor var_8347 = mul(x = freqs_39, y = ts_119)[name = tensor("op_8347")]; + tensor rotr_39 = cos(x = var_8347)[name = tensor("rotr_39")]; + tensor roti_39 = sin(x = var_8347)[name = tensor("roti_39")]; + tensor var_8351 = mul(x = var_8317, y = rotr_39)[name = tensor("op_8351")]; + tensor var_8352 = mul(x = var_8325, y = roti_39)[name = tensor("op_8352")]; + tensor qor_77 = sub(x = var_8351, y = var_8352)[name = tensor("qor_77")]; + tensor var_8355 = mul(x = var_8317, y = roti_39)[name = tensor("op_8355")]; + tensor var_8356 = mul(x = var_8325, y = rotr_39)[name = tensor("op_8356")]; + tensor qoi_77 = add(x = var_8355, y = var_8356)[name = tensor("qoi_77")]; + tensor var_8359 = mul(x = var_8333, y = rotr_39)[name = tensor("op_8359")]; + tensor var_8360 = mul(x = var_8341, y = roti_39)[name = tensor("op_8360")]; + tensor kor_77 = sub(x = var_8359, y = var_8360)[name = tensor("kor_77")]; + tensor var_8363 = mul(x = var_8333, y = roti_39)[name = tensor("op_8363")]; + tensor var_8364 = mul(x = var_8341, y = rotr_39)[name = tensor("op_8364")]; + tensor koi_77 = add(x = var_8363, y = var_8364)[name = tensor("koi_77")]; + tensor qo_39_axis_0 = const()[name = tensor("qo_39_axis_0"), val = tensor(-1)]; + tensor qo_39 = stack(axis = qo_39_axis_0, values = (qor_77, qoi_77))[name = tensor("qo_39")]; + tensor ko_39_axis_0 = const()[name = tensor("ko_39_axis_0"), val = tensor(-1)]; + tensor ko_39 = stack(axis = ko_39_axis_0, values = (kor_77, koi_77))[name = tensor("ko_39")]; + tensor var_8393 = const()[name = tensor("op_8393"), val = tensor([1, 1, 16, 64])]; + tensor q_117 = reshape(shape = var_8393, x = qo_39)[name = tensor("q_117")]; + tensor var_8395 = const()[name = tensor("op_8395"), val = tensor([1, 1, 16, 64])]; + tensor k_79 = reshape(shape = var_8395, x = ko_39)[name = tensor("k_79")]; + tensor _inversed_8417_y_0 = const()[name = tensor("_inversed_8417_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_8417 = mul(x = ts_119, y = _inversed_8417_y_0)[name = tensor("_inversed_8417")]; + tensor var_8418 = floor(x = _inversed_8417)[name = tensor("op_8418")]; + tensor var_8419 = const()[name = tensor("op_8419"), val = tensor(0x1p+9)]; + tensor var_8420 = mul(x = var_8418, y = var_8419)[name = tensor("op_8420")]; + tensor write_indices_float_79 = sub(x = ts_119, y = var_8420)[name = tensor("write_indices_float_79")]; + tensor var_8427_dtype_0 = const()[name = tensor("op_8427_dtype_0"), val = tensor("int32")]; + tensor write_indices_39_reps_0 = const()[name = tensor("write_indices_39_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_8427 = cast(dtype = var_8427_dtype_0, x = write_indices_float_79)[name = tensor("cast_436")]; + tensor write_indices_39 = tile(reps = write_indices_39_reps_0, x = var_8427)[name = tensor("write_indices_39")]; + tensor var_8435_begin_0 = const()[name = tensor("op_8435_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8435_end_0 = const()[name = tensor("op_8435_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_8435_end_mask_0 = const()[name = tensor("op_8435_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8435_squeeze_mask_0 = const()[name = tensor("op_8435_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8435 = slice_by_index(begin = var_8435_begin_0, end = var_8435_end_0, end_mask = var_8435_end_mask_0, squeeze_mask = var_8435_squeeze_mask_0, x = cache19)[name = tensor("op_8435")]; + tensor var_8437_axis_0 = const()[name = tensor("op_8437_axis_0"), val = tensor(1)]; + tensor var_8437_mode_0 = const()[name = tensor("op_8437_mode_0"), val = tensor("update")]; + tensor var_8437_validate_indices_0 = const()[name = tensor("op_8437_validate_indices_0"), val = tensor(false)]; + tensor var_8437 = scatter_along_axis(axis = var_8437_axis_0, data = var_8435, indices = write_indices_39, mode = var_8437_mode_0, updates = k_79, validate_indices = var_8437_validate_indices_0)[name = tensor("op_8437")]; + tensor concat_135 = const()[name = tensor("concat_135"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_136 = const()[name = tensor("concat_136"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_39_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_39_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_86 = const()[name = tensor("shape_86"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_38 = const()[name = tensor("reduce_prod_38"), val = tensor(1048576)]; + tensor range_1d_38_start_0 = const()[name = tensor("range_1d_38_start_0"), val = tensor(0)]; + tensor range_1d_38_step_0 = const()[name = tensor("range_1d_38_step_0"), val = tensor(1)]; + tensor range_1d_38 = range_1d(end = reduce_prod_38, start = range_1d_38_start_0, step = range_1d_38_step_0)[name = tensor("range_1d_38")]; + tensor reshape_190 = reshape(shape = shape_86, x = range_1d_38)[name = tensor("reshape_190")]; + tensor slice_by_index_38 = slice_by_index(begin = concat_135, begin_mask = new_cache_39_internal_tensor_assign_1_begin_mask_0, end = concat_136, end_mask = new_cache_39_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_39_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_39_internal_tensor_assign_1_stride_0, x = reshape_190)[name = tensor("slice_by_index_38")]; + tensor reshape_191_shape_0 = const()[name = tensor("reshape_191_shape_0"), val = tensor([-1])]; + tensor reshape_191 = reshape(shape = reshape_191_shape_0, x = slice_by_index_38)[name = tensor("reshape_191")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([-1])]; + tensor reshape_192 = reshape(shape = reshape_192_shape_0, x = var_8437)[name = tensor("reshape_192")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([-1])]; + tensor reshape_193 = reshape(shape = reshape_193_shape_0, x = cache19)[name = tensor("reshape_193")]; + tensor scatter_38_mode_0 = const()[name = tensor("scatter_38_mode_0"), val = tensor("update")]; + tensor scatter_38_axis_0 = const()[name = tensor("scatter_38_axis_0"), val = tensor(0)]; + tensor scatter_38_validate_indices_0 = const()[name = tensor("scatter_38_validate_indices_0"), val = tensor(false)]; + tensor scatter_38 = scatter(axis = scatter_38_axis_0, data = reshape_193, indices = reshape_191, mode = scatter_38_mode_0, updates = reshape_192, validate_indices = scatter_38_validate_indices_0)[name = tensor("scatter_38")]; + tensor reshape_194 = reshape(shape = shape_86, x = scatter_38)[name = tensor("reshape_194")]; + tensor var_8445_begin_0 = const()[name = tensor("op_8445_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_8445_end_0 = const()[name = tensor("op_8445_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_8445_end_mask_0 = const()[name = tensor("op_8445_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8445_squeeze_mask_0 = const()[name = tensor("op_8445_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8445 = slice_by_index(begin = var_8445_begin_0, end = var_8445_end_0, end_mask = var_8445_end_mask_0, squeeze_mask = var_8445_squeeze_mask_0, x = reshape_194)[name = tensor("op_8445")]; + tensor var_8447_axis_0 = const()[name = tensor("op_8447_axis_0"), val = tensor(1)]; + tensor var_8447_mode_0 = const()[name = tensor("op_8447_mode_0"), val = tensor("update")]; + tensor var_8447_validate_indices_0 = const()[name = tensor("op_8447_validate_indices_0"), val = tensor(false)]; + tensor var_8447 = scatter_along_axis(axis = var_8447_axis_0, data = var_8445, indices = write_indices_39, mode = var_8447_mode_0, updates = v_39, validate_indices = var_8447_validate_indices_0)[name = tensor("op_8447")]; + tensor concat_137 = const()[name = tensor("concat_137"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_138 = const()[name = tensor("concat_138"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_39_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_39_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_39_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_39_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_87 = const()[name = tensor("shape_87"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_39 = const()[name = tensor("reduce_prod_39"), val = tensor(1048576)]; + tensor range_1d_39_start_0 = const()[name = tensor("range_1d_39_start_0"), val = tensor(0)]; + tensor range_1d_39_step_0 = const()[name = tensor("range_1d_39_step_0"), val = tensor(1)]; + tensor range_1d_39 = range_1d(end = reduce_prod_39, start = range_1d_39_start_0, step = range_1d_39_step_0)[name = tensor("range_1d_39")]; + tensor reshape_195 = reshape(shape = shape_87, x = range_1d_39)[name = tensor("reshape_195")]; + tensor slice_by_index_39 = slice_by_index(begin = concat_137, begin_mask = new_cache_39_internal_tensor_assign_2_begin_mask_0, end = concat_138, end_mask = new_cache_39_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_39_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_39_internal_tensor_assign_2_stride_0, x = reshape_195)[name = tensor("slice_by_index_39")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([-1])]; + tensor reshape_196 = reshape(shape = reshape_196_shape_0, x = slice_by_index_39)[name = tensor("reshape_196")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([-1])]; + tensor reshape_197 = reshape(shape = reshape_197_shape_0, x = var_8447)[name = tensor("reshape_197")]; + tensor reshape_198_shape_0 = const()[name = tensor("reshape_198_shape_0"), val = tensor([-1])]; + tensor reshape_198 = reshape(shape = reshape_198_shape_0, x = reshape_194)[name = tensor("reshape_198")]; + tensor scatter_39_mode_0 = const()[name = tensor("scatter_39_mode_0"), val = tensor("update")]; + tensor scatter_39_axis_0 = const()[name = tensor("scatter_39_axis_0"), val = tensor(0)]; + tensor scatter_39_validate_indices_0 = const()[name = tensor("scatter_39_validate_indices_0"), val = tensor(false)]; + tensor scatter_39 = scatter(axis = scatter_39_axis_0, data = reshape_198, indices = reshape_196, mode = scatter_39_mode_0, updates = reshape_197, validate_indices = scatter_39_validate_indices_0)[name = tensor("scatter_39")]; + tensor new_cache_39_internal_tensor_assign_2 = reshape(shape = shape_87, x = scatter_39)[name = tensor("reshape_199")]; + tensor keys_115_begin_0 = const()[name = tensor("keys_115_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_115_end_0 = const()[name = tensor("keys_115_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_115_end_mask_0 = const()[name = tensor("keys_115_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_115_squeeze_mask_0 = const()[name = tensor("keys_115_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_115 = slice_by_index(begin = keys_115_begin_0, end = keys_115_end_0, end_mask = keys_115_end_mask_0, squeeze_mask = keys_115_squeeze_mask_0, x = new_cache_39_internal_tensor_assign_2)[name = tensor("keys_115")]; + tensor values_115_begin_0 = const()[name = tensor("values_115_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_115_end_0 = const()[name = tensor("values_115_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_115_end_mask_0 = const()[name = tensor("values_115_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_115_squeeze_mask_0 = const()[name = tensor("values_115_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_115 = slice_by_index(begin = values_115_begin_0, end = values_115_end_0, end_mask = values_115_end_mask_0, squeeze_mask = values_115_squeeze_mask_0, x = new_cache_39_internal_tensor_assign_2)[name = tensor("values_115")]; + tensor var_8459 = not_equal(x = keys_115, y = keys_115)[name = tensor("op_8459")]; + tensor keys_117 = select(a = var_504, b = keys_115, cond = var_8459)[name = tensor("keys_117")]; + tensor var_8467 = not_equal(x = values_115, y = values_115)[name = tensor("op_8467")]; + tensor values_117 = select(a = var_504, b = values_115, cond = var_8467)[name = tensor("values_117")]; + tensor var_8491 = const()[name = tensor("op_8491"), val = tensor([0, 2, 1, 3])]; + tensor var_8504 = const()[name = tensor("op_8504"), val = tensor([1, 1, 1])]; + tensor var_8505 = reshape(shape = var_8504, x = position19)[name = tensor("op_8505")]; + tensor var_8522 = const()[name = tensor("op_8522"), val = tensor(0x1p+0)]; + tensor valid_len_39 = add(x = var_8505, y = var_8522)[name = tensor("valid_len_39")]; + tensor valid_mask_39 = less(x = k_positions_1_promoted, y = valid_len_39)[name = tensor("valid_mask_39")]; + tensor causal_mask_39 = less_equal(x = k_positions_1_promoted, y = var_8505)[name = tensor("causal_mask_39")]; + tensor attn_mask_77 = logical_and(x = valid_mask_39, y = causal_mask_39)[name = tensor("attn_mask_77")]; + tensor attn_mask_79_axes_0 = const()[name = tensor("attn_mask_79_axes_0"), val = tensor([1])]; + tensor attn_mask_79 = expand_dims(axes = attn_mask_79_axes_0, x = attn_mask_77)[name = tensor("attn_mask_79")]; + tensor var_8534 = const()[name = tensor("op_8534"), val = tensor([0x1.fffe5cp-4])]; + tensor var_8540_transpose_x_0 = const()[name = tensor("op_8540_transpose_x_0"), val = tensor(false)]; + tensor var_8540_transpose_y_0 = const()[name = tensor("op_8540_transpose_y_0"), val = tensor(false)]; + tensor transpose_110_perm_0 = const()[name = tensor("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = tensor("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = keys_117)[name = tensor("transpose_137")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = q_117)[name = tensor("transpose_138")]; + tensor var_8540 = matmul(transpose_x = var_8540_transpose_x_0, transpose_y = var_8540_transpose_y_0, x = transpose_110, y = transpose_111)[name = tensor("op_8540")]; + tensor attn_weights_115 = mul(x = var_8540, y = var_8534)[name = tensor("attn_weights_115")]; + tensor var_8542 = logical_not(x = attn_mask_79)[name = tensor("op_8542")]; + tensor var_8543 = const()[name = tensor("op_8543"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_117 = select(a = var_8543, b = attn_weights_115, cond = var_8542)[name = tensor("attn_weights_117")]; + tensor var_8545 = const()[name = tensor("op_8545"), val = tensor(-1)]; + tensor attn_weights_119 = softmax(axis = var_8545, x = attn_weights_117)[name = tensor("attn_weights_119")]; + tensor attn_output_39_transpose_x_0 = const()[name = tensor("attn_output_39_transpose_x_0"), val = tensor(false)]; + tensor attn_output_39_transpose_y_0 = const()[name = tensor("attn_output_39_transpose_y_0"), val = tensor(false)]; + tensor values_119 = transpose(perm = var_8491, x = values_117)[name = tensor("transpose_139")]; + tensor attn_output_39 = matmul(transpose_x = attn_output_39_transpose_x_0, transpose_y = attn_output_39_transpose_y_0, x = attn_weights_119, y = values_119)[name = tensor("attn_output_39")]; + tensor var_8553 = const()[name = tensor("op_8553"), val = tensor([0, 2, 1, 3])]; + tensor var_8556 = const()[name = tensor("op_8556"), val = tensor([1, 1, 1024])]; + tensor var_8554 = transpose(perm = var_8553, x = attn_output_39)[name = tensor("transpose_136")]; + tensor input_195 = reshape(shape = var_8556, x = var_8554)[name = tensor("input_195")]; + tensor attn_out_39 = linear(bias = linear_0_bias_0, weight = attn19_out_proj_weight, x = input_195)[name = tensor("linear_78")]; + tensor var_8562 = const()[name = tensor("op_8562"), val = tensor(0x1p+0)]; + tensor var_8563 = add(x = position19, y = var_8562)[name = tensor("op_8563")]; + tensor input_197 = add(x = input_193, y = attn_out_39)[name = tensor("input_197")]; + tensor var_8567 = const()[name = tensor("op_8567"), val = tensor(0x1.4f8b58p-17)]; + tensor input_199_axes_0 = const()[name = tensor("input_199_axes_0"), val = tensor([-1])]; + tensor input_199 = layer_norm(axes = input_199_axes_0, beta = norm19_2_bias, epsilon = var_8567, gamma = norm19_2_weight, x = input_197)[name = tensor("input_199")]; + tensor var_8575 = linear(bias = linear_3_bias_0, weight = linear19_1_weight, x = input_199)[name = tensor("linear_79")]; + tensor input_201_mode_0 = const()[name = tensor("input_201_mode_0"), val = tensor("EXACT")]; + tensor input_201 = gelu(mode = input_201_mode_0, x = var_8575)[name = tensor("input_201")]; + tensor ffn_out_39 = linear(bias = linear_0_bias_0, weight = linear19_2_weight, x = input_201)[name = tensor("linear_80")]; + tensor input_203 = add(x = input_197, y = ffn_out_39)[name = tensor("input_203")]; + tensor var_8584 = const()[name = tensor("op_8584"), val = tensor(0x1.4f8b58p-17)]; + tensor x_41_axes_0 = const()[name = tensor("x_41_axes_0"), val = tensor([-1])]; + tensor x_41 = layer_norm(axes = x_41_axes_0, beta = norm20_1_bias, epsilon = var_8584, gamma = norm20_1_weight, x = input_203)[name = tensor("x_41")]; + tensor var_8616 = linear(bias = linear_1_bias_0, weight = attn20_in_proj_weight, x = x_41)[name = tensor("linear_81")]; + tensor var_8620 = const()[name = tensor("op_8620"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_41 = reshape(shape = var_8620, x = var_8616)[name = tensor("qkv_41")]; + tensor q_121_begin_0 = const()[name = tensor("q_121_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_121_end_0 = const()[name = tensor("q_121_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_121_end_mask_0 = const()[name = tensor("q_121_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_121_squeeze_mask_0 = const()[name = tensor("q_121_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_121 = slice_by_index(begin = q_121_begin_0, end = q_121_end_0, end_mask = q_121_end_mask_0, squeeze_mask = q_121_squeeze_mask_0, x = qkv_41)[name = tensor("q_121")]; + tensor k_81_begin_0 = const()[name = tensor("k_81_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_81_end_0 = const()[name = tensor("k_81_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_81_end_mask_0 = const()[name = tensor("k_81_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_81_squeeze_mask_0 = const()[name = tensor("k_81_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_81 = slice_by_index(begin = k_81_begin_0, end = k_81_end_0, end_mask = k_81_end_mask_0, squeeze_mask = k_81_squeeze_mask_0, x = qkv_41)[name = tensor("k_81")]; + tensor v_41_begin_0 = const()[name = tensor("v_41_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_41_end_0 = const()[name = tensor("v_41_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_41_end_mask_0 = const()[name = tensor("v_41_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_41_squeeze_mask_0 = const()[name = tensor("v_41_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_41 = slice_by_index(begin = v_41_begin_0, end = v_41_end_0, end_mask = v_41_end_mask_0, squeeze_mask = v_41_squeeze_mask_0, x = qkv_41)[name = tensor("v_41")]; + tensor freqs_41 = const()[name = tensor("freqs_41"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210644864)))]; + tensor var_8724 = const()[name = tensor("op_8724"), val = tensor([1, 1, 1, 1])]; + tensor ts_125 = reshape(shape = var_8724, x = position20)[name = tensor("ts_125")]; + tensor var_8728 = const()[name = tensor("op_8728"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_41 = reshape(shape = var_8728, x = q_121)[name = tensor("q_complex_41")]; + tensor var_8732 = const()[name = tensor("op_8732"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_41 = reshape(shape = var_8732, x = k_81)[name = tensor("k_complex_41")]; + tensor var_8736_begin_0 = const()[name = tensor("op_8736_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8736_end_0 = const()[name = tensor("op_8736_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8736_end_mask_0 = const()[name = tensor("op_8736_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8736_squeeze_mask_0 = const()[name = tensor("op_8736_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8736 = slice_by_index(begin = var_8736_begin_0, end = var_8736_end_0, end_mask = var_8736_end_mask_0, squeeze_mask = var_8736_squeeze_mask_0, x = q_complex_41)[name = tensor("op_8736")]; + tensor var_8744_begin_0 = const()[name = tensor("op_8744_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8744_end_0 = const()[name = tensor("op_8744_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8744_end_mask_0 = const()[name = tensor("op_8744_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8744_squeeze_mask_0 = const()[name = tensor("op_8744_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8744 = slice_by_index(begin = var_8744_begin_0, end = var_8744_end_0, end_mask = var_8744_end_mask_0, squeeze_mask = var_8744_squeeze_mask_0, x = q_complex_41)[name = tensor("op_8744")]; + tensor var_8752_begin_0 = const()[name = tensor("op_8752_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8752_end_0 = const()[name = tensor("op_8752_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_8752_end_mask_0 = const()[name = tensor("op_8752_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8752_squeeze_mask_0 = const()[name = tensor("op_8752_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8752 = slice_by_index(begin = var_8752_begin_0, end = var_8752_end_0, end_mask = var_8752_end_mask_0, squeeze_mask = var_8752_squeeze_mask_0, x = k_complex_41)[name = tensor("op_8752")]; + tensor var_8760_begin_0 = const()[name = tensor("op_8760_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_8760_end_0 = const()[name = tensor("op_8760_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_8760_end_mask_0 = const()[name = tensor("op_8760_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_8760_squeeze_mask_0 = const()[name = tensor("op_8760_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_8760 = slice_by_index(begin = var_8760_begin_0, end = var_8760_end_0, end_mask = var_8760_end_mask_0, squeeze_mask = var_8760_squeeze_mask_0, x = k_complex_41)[name = tensor("op_8760")]; + tensor var_8766 = mul(x = freqs_41, y = ts_125)[name = tensor("op_8766")]; + tensor rotr_41 = cos(x = var_8766)[name = tensor("rotr_41")]; + tensor roti_41 = sin(x = var_8766)[name = tensor("roti_41")]; + tensor var_8770 = mul(x = var_8736, y = rotr_41)[name = tensor("op_8770")]; + tensor var_8771 = mul(x = var_8744, y = roti_41)[name = tensor("op_8771")]; + tensor qor_81 = sub(x = var_8770, y = var_8771)[name = tensor("qor_81")]; + tensor var_8774 = mul(x = var_8736, y = roti_41)[name = tensor("op_8774")]; + tensor var_8775 = mul(x = var_8744, y = rotr_41)[name = tensor("op_8775")]; + tensor qoi_81 = add(x = var_8774, y = var_8775)[name = tensor("qoi_81")]; + tensor var_8778 = mul(x = var_8752, y = rotr_41)[name = tensor("op_8778")]; + tensor var_8779 = mul(x = var_8760, y = roti_41)[name = tensor("op_8779")]; + tensor kor_81 = sub(x = var_8778, y = var_8779)[name = tensor("kor_81")]; + tensor var_8782 = mul(x = var_8752, y = roti_41)[name = tensor("op_8782")]; + tensor var_8783 = mul(x = var_8760, y = rotr_41)[name = tensor("op_8783")]; + tensor koi_81 = add(x = var_8782, y = var_8783)[name = tensor("koi_81")]; + tensor qo_41_axis_0 = const()[name = tensor("qo_41_axis_0"), val = tensor(-1)]; + tensor qo_41 = stack(axis = qo_41_axis_0, values = (qor_81, qoi_81))[name = tensor("qo_41")]; + tensor ko_41_axis_0 = const()[name = tensor("ko_41_axis_0"), val = tensor(-1)]; + tensor ko_41 = stack(axis = ko_41_axis_0, values = (kor_81, koi_81))[name = tensor("ko_41")]; + tensor var_8812 = const()[name = tensor("op_8812"), val = tensor([1, 1, 16, 64])]; + tensor q_123 = reshape(shape = var_8812, x = qo_41)[name = tensor("q_123")]; + tensor var_8814 = const()[name = tensor("op_8814"), val = tensor([1, 1, 16, 64])]; + tensor k_83 = reshape(shape = var_8814, x = ko_41)[name = tensor("k_83")]; + tensor _inversed_8836_y_0 = const()[name = tensor("_inversed_8836_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_8836 = mul(x = ts_125, y = _inversed_8836_y_0)[name = tensor("_inversed_8836")]; + tensor var_8837 = floor(x = _inversed_8836)[name = tensor("op_8837")]; + tensor var_8838 = const()[name = tensor("op_8838"), val = tensor(0x1p+9)]; + tensor var_8839 = mul(x = var_8837, y = var_8838)[name = tensor("op_8839")]; + tensor write_indices_float_83 = sub(x = ts_125, y = var_8839)[name = tensor("write_indices_float_83")]; + tensor var_8846_dtype_0 = const()[name = tensor("op_8846_dtype_0"), val = tensor("int32")]; + tensor write_indices_41_reps_0 = const()[name = tensor("write_indices_41_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_8846 = cast(dtype = var_8846_dtype_0, x = write_indices_float_83)[name = tensor("cast_435")]; + tensor write_indices_41 = tile(reps = write_indices_41_reps_0, x = var_8846)[name = tensor("write_indices_41")]; + tensor var_8854_begin_0 = const()[name = tensor("op_8854_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_8854_end_0 = const()[name = tensor("op_8854_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_8854_end_mask_0 = const()[name = tensor("op_8854_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8854_squeeze_mask_0 = const()[name = tensor("op_8854_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8854 = slice_by_index(begin = var_8854_begin_0, end = var_8854_end_0, end_mask = var_8854_end_mask_0, squeeze_mask = var_8854_squeeze_mask_0, x = cache20)[name = tensor("op_8854")]; + tensor var_8856_axis_0 = const()[name = tensor("op_8856_axis_0"), val = tensor(1)]; + tensor var_8856_mode_0 = const()[name = tensor("op_8856_mode_0"), val = tensor("update")]; + tensor var_8856_validate_indices_0 = const()[name = tensor("op_8856_validate_indices_0"), val = tensor(false)]; + tensor var_8856 = scatter_along_axis(axis = var_8856_axis_0, data = var_8854, indices = write_indices_41, mode = var_8856_mode_0, updates = k_83, validate_indices = var_8856_validate_indices_0)[name = tensor("op_8856")]; + tensor concat_142 = const()[name = tensor("concat_142"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_143 = const()[name = tensor("concat_143"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_41_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_41_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_88 = const()[name = tensor("shape_88"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_40 = const()[name = tensor("reduce_prod_40"), val = tensor(1048576)]; + tensor range_1d_40_start_0 = const()[name = tensor("range_1d_40_start_0"), val = tensor(0)]; + tensor range_1d_40_step_0 = const()[name = tensor("range_1d_40_step_0"), val = tensor(1)]; + tensor range_1d_40 = range_1d(end = reduce_prod_40, start = range_1d_40_start_0, step = range_1d_40_step_0)[name = tensor("range_1d_40")]; + tensor reshape_200 = reshape(shape = shape_88, x = range_1d_40)[name = tensor("reshape_200")]; + tensor slice_by_index_40 = slice_by_index(begin = concat_142, begin_mask = new_cache_41_internal_tensor_assign_1_begin_mask_0, end = concat_143, end_mask = new_cache_41_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_41_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_41_internal_tensor_assign_1_stride_0, x = reshape_200)[name = tensor("slice_by_index_40")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([-1])]; + tensor reshape_201 = reshape(shape = reshape_201_shape_0, x = slice_by_index_40)[name = tensor("reshape_201")]; + tensor reshape_202_shape_0 = const()[name = tensor("reshape_202_shape_0"), val = tensor([-1])]; + tensor reshape_202 = reshape(shape = reshape_202_shape_0, x = var_8856)[name = tensor("reshape_202")]; + tensor reshape_203_shape_0 = const()[name = tensor("reshape_203_shape_0"), val = tensor([-1])]; + tensor reshape_203 = reshape(shape = reshape_203_shape_0, x = cache20)[name = tensor("reshape_203")]; + tensor scatter_40_mode_0 = const()[name = tensor("scatter_40_mode_0"), val = tensor("update")]; + tensor scatter_40_axis_0 = const()[name = tensor("scatter_40_axis_0"), val = tensor(0)]; + tensor scatter_40_validate_indices_0 = const()[name = tensor("scatter_40_validate_indices_0"), val = tensor(false)]; + tensor scatter_40 = scatter(axis = scatter_40_axis_0, data = reshape_203, indices = reshape_201, mode = scatter_40_mode_0, updates = reshape_202, validate_indices = scatter_40_validate_indices_0)[name = tensor("scatter_40")]; + tensor reshape_204 = reshape(shape = shape_88, x = scatter_40)[name = tensor("reshape_204")]; + tensor var_8864_begin_0 = const()[name = tensor("op_8864_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_8864_end_0 = const()[name = tensor("op_8864_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_8864_end_mask_0 = const()[name = tensor("op_8864_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_8864_squeeze_mask_0 = const()[name = tensor("op_8864_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_8864 = slice_by_index(begin = var_8864_begin_0, end = var_8864_end_0, end_mask = var_8864_end_mask_0, squeeze_mask = var_8864_squeeze_mask_0, x = reshape_204)[name = tensor("op_8864")]; + tensor var_8866_axis_0 = const()[name = tensor("op_8866_axis_0"), val = tensor(1)]; + tensor var_8866_mode_0 = const()[name = tensor("op_8866_mode_0"), val = tensor("update")]; + tensor var_8866_validate_indices_0 = const()[name = tensor("op_8866_validate_indices_0"), val = tensor(false)]; + tensor var_8866 = scatter_along_axis(axis = var_8866_axis_0, data = var_8864, indices = write_indices_41, mode = var_8866_mode_0, updates = v_41, validate_indices = var_8866_validate_indices_0)[name = tensor("op_8866")]; + tensor concat_144 = const()[name = tensor("concat_144"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_145 = const()[name = tensor("concat_145"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_41_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_41_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_41_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_41_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_89 = const()[name = tensor("shape_89"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_41 = const()[name = tensor("reduce_prod_41"), val = tensor(1048576)]; + tensor range_1d_41_start_0 = const()[name = tensor("range_1d_41_start_0"), val = tensor(0)]; + tensor range_1d_41_step_0 = const()[name = tensor("range_1d_41_step_0"), val = tensor(1)]; + tensor range_1d_41 = range_1d(end = reduce_prod_41, start = range_1d_41_start_0, step = range_1d_41_step_0)[name = tensor("range_1d_41")]; + tensor reshape_205 = reshape(shape = shape_89, x = range_1d_41)[name = tensor("reshape_205")]; + tensor slice_by_index_41 = slice_by_index(begin = concat_144, begin_mask = new_cache_41_internal_tensor_assign_2_begin_mask_0, end = concat_145, end_mask = new_cache_41_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_41_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_41_internal_tensor_assign_2_stride_0, x = reshape_205)[name = tensor("slice_by_index_41")]; + tensor reshape_206_shape_0 = const()[name = tensor("reshape_206_shape_0"), val = tensor([-1])]; + tensor reshape_206 = reshape(shape = reshape_206_shape_0, x = slice_by_index_41)[name = tensor("reshape_206")]; + tensor reshape_207_shape_0 = const()[name = tensor("reshape_207_shape_0"), val = tensor([-1])]; + tensor reshape_207 = reshape(shape = reshape_207_shape_0, x = var_8866)[name = tensor("reshape_207")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([-1])]; + tensor reshape_208 = reshape(shape = reshape_208_shape_0, x = reshape_204)[name = tensor("reshape_208")]; + tensor scatter_41_mode_0 = const()[name = tensor("scatter_41_mode_0"), val = tensor("update")]; + tensor scatter_41_axis_0 = const()[name = tensor("scatter_41_axis_0"), val = tensor(0)]; + tensor scatter_41_validate_indices_0 = const()[name = tensor("scatter_41_validate_indices_0"), val = tensor(false)]; + tensor scatter_41 = scatter(axis = scatter_41_axis_0, data = reshape_208, indices = reshape_206, mode = scatter_41_mode_0, updates = reshape_207, validate_indices = scatter_41_validate_indices_0)[name = tensor("scatter_41")]; + tensor new_cache_41_internal_tensor_assign_2 = reshape(shape = shape_89, x = scatter_41)[name = tensor("reshape_209")]; + tensor keys_121_begin_0 = const()[name = tensor("keys_121_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_121_end_0 = const()[name = tensor("keys_121_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_121_end_mask_0 = const()[name = tensor("keys_121_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_121_squeeze_mask_0 = const()[name = tensor("keys_121_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_121 = slice_by_index(begin = keys_121_begin_0, end = keys_121_end_0, end_mask = keys_121_end_mask_0, squeeze_mask = keys_121_squeeze_mask_0, x = new_cache_41_internal_tensor_assign_2)[name = tensor("keys_121")]; + tensor values_121_begin_0 = const()[name = tensor("values_121_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_121_end_0 = const()[name = tensor("values_121_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_121_end_mask_0 = const()[name = tensor("values_121_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_121_squeeze_mask_0 = const()[name = tensor("values_121_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_121 = slice_by_index(begin = values_121_begin_0, end = values_121_end_0, end_mask = values_121_end_mask_0, squeeze_mask = values_121_squeeze_mask_0, x = new_cache_41_internal_tensor_assign_2)[name = tensor("values_121")]; + tensor var_8878 = not_equal(x = keys_121, y = keys_121)[name = tensor("op_8878")]; + tensor keys_123 = select(a = var_504, b = keys_121, cond = var_8878)[name = tensor("keys_123")]; + tensor var_8886 = not_equal(x = values_121, y = values_121)[name = tensor("op_8886")]; + tensor values_123 = select(a = var_504, b = values_121, cond = var_8886)[name = tensor("values_123")]; + tensor var_8910 = const()[name = tensor("op_8910"), val = tensor([0, 2, 1, 3])]; + tensor var_8923 = const()[name = tensor("op_8923"), val = tensor([1, 1, 1])]; + tensor var_8924 = reshape(shape = var_8923, x = position20)[name = tensor("op_8924")]; + tensor var_8941 = const()[name = tensor("op_8941"), val = tensor(0x1p+0)]; + tensor valid_len_41 = add(x = var_8924, y = var_8941)[name = tensor("valid_len_41")]; + tensor valid_mask_41 = less(x = k_positions_1_promoted, y = valid_len_41)[name = tensor("valid_mask_41")]; + tensor causal_mask_41 = less_equal(x = k_positions_1_promoted, y = var_8924)[name = tensor("causal_mask_41")]; + tensor attn_mask_81 = logical_and(x = valid_mask_41, y = causal_mask_41)[name = tensor("attn_mask_81")]; + tensor attn_mask_83_axes_0 = const()[name = tensor("attn_mask_83_axes_0"), val = tensor([1])]; + tensor attn_mask_83 = expand_dims(axes = attn_mask_83_axes_0, x = attn_mask_81)[name = tensor("attn_mask_83")]; + tensor var_8953 = const()[name = tensor("op_8953"), val = tensor([0x1.fffe5cp-4])]; + tensor var_8959_transpose_x_0 = const()[name = tensor("op_8959_transpose_x_0"), val = tensor(false)]; + tensor var_8959_transpose_y_0 = const()[name = tensor("op_8959_transpose_y_0"), val = tensor(false)]; + tensor transpose_112_perm_0 = const()[name = tensor("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = tensor("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = keys_123)[name = tensor("transpose_133")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = q_123)[name = tensor("transpose_134")]; + tensor var_8959 = matmul(transpose_x = var_8959_transpose_x_0, transpose_y = var_8959_transpose_y_0, x = transpose_112, y = transpose_113)[name = tensor("op_8959")]; + tensor attn_weights_121 = mul(x = var_8959, y = var_8953)[name = tensor("attn_weights_121")]; + tensor var_8961 = logical_not(x = attn_mask_83)[name = tensor("op_8961")]; + tensor var_8962 = const()[name = tensor("op_8962"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_123 = select(a = var_8962, b = attn_weights_121, cond = var_8961)[name = tensor("attn_weights_123")]; + tensor var_8964 = const()[name = tensor("op_8964"), val = tensor(-1)]; + tensor attn_weights_125 = softmax(axis = var_8964, x = attn_weights_123)[name = tensor("attn_weights_125")]; + tensor attn_output_41_transpose_x_0 = const()[name = tensor("attn_output_41_transpose_x_0"), val = tensor(false)]; + tensor attn_output_41_transpose_y_0 = const()[name = tensor("attn_output_41_transpose_y_0"), val = tensor(false)]; + tensor values_125 = transpose(perm = var_8910, x = values_123)[name = tensor("transpose_135")]; + tensor attn_output_41 = matmul(transpose_x = attn_output_41_transpose_x_0, transpose_y = attn_output_41_transpose_y_0, x = attn_weights_125, y = values_125)[name = tensor("attn_output_41")]; + tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([0, 2, 1, 3])]; + tensor var_8975 = const()[name = tensor("op_8975"), val = tensor([1, 1, 1024])]; + tensor var_8973 = transpose(perm = var_8972, x = attn_output_41)[name = tensor("transpose_132")]; + tensor input_205 = reshape(shape = var_8975, x = var_8973)[name = tensor("input_205")]; + tensor attn_out_41 = linear(bias = linear_0_bias_0, weight = attn20_out_proj_weight, x = input_205)[name = tensor("linear_82")]; + tensor var_8981 = const()[name = tensor("op_8981"), val = tensor(0x1p+0)]; + tensor var_8982 = add(x = position20, y = var_8981)[name = tensor("op_8982")]; + tensor input_207 = add(x = input_203, y = attn_out_41)[name = tensor("input_207")]; + tensor var_8986 = const()[name = tensor("op_8986"), val = tensor(0x1.4f8b58p-17)]; + tensor input_209_axes_0 = const()[name = tensor("input_209_axes_0"), val = tensor([-1])]; + tensor input_209 = layer_norm(axes = input_209_axes_0, beta = norm20_2_bias, epsilon = var_8986, gamma = norm20_2_weight, x = input_207)[name = tensor("input_209")]; + tensor var_8994 = linear(bias = linear_3_bias_0, weight = linear20_1_weight, x = input_209)[name = tensor("linear_83")]; + tensor input_211_mode_0 = const()[name = tensor("input_211_mode_0"), val = tensor("EXACT")]; + tensor input_211 = gelu(mode = input_211_mode_0, x = var_8994)[name = tensor("input_211")]; + tensor ffn_out_41 = linear(bias = linear_0_bias_0, weight = linear20_2_weight, x = input_211)[name = tensor("linear_84")]; + tensor input_213 = add(x = input_207, y = ffn_out_41)[name = tensor("input_213")]; + tensor var_9003 = const()[name = tensor("op_9003"), val = tensor(0x1.4f8b58p-17)]; + tensor x_43_axes_0 = const()[name = tensor("x_43_axes_0"), val = tensor([-1])]; + tensor x_43 = layer_norm(axes = x_43_axes_0, beta = norm21_1_bias, epsilon = var_9003, gamma = norm21_1_weight, x = input_213)[name = tensor("x_43")]; + tensor var_9035 = linear(bias = linear_1_bias_0, weight = attn21_in_proj_weight, x = x_43)[name = tensor("linear_85")]; + tensor var_9039 = const()[name = tensor("op_9039"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_43 = reshape(shape = var_9039, x = var_9035)[name = tensor("qkv_43")]; + tensor q_127_begin_0 = const()[name = tensor("q_127_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_127_end_0 = const()[name = tensor("q_127_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_127_end_mask_0 = const()[name = tensor("q_127_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_127_squeeze_mask_0 = const()[name = tensor("q_127_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_127 = slice_by_index(begin = q_127_begin_0, end = q_127_end_0, end_mask = q_127_end_mask_0, squeeze_mask = q_127_squeeze_mask_0, x = qkv_43)[name = tensor("q_127")]; + tensor k_85_begin_0 = const()[name = tensor("k_85_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_85_end_0 = const()[name = tensor("k_85_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_85_end_mask_0 = const()[name = tensor("k_85_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_85_squeeze_mask_0 = const()[name = tensor("k_85_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_85 = slice_by_index(begin = k_85_begin_0, end = k_85_end_0, end_mask = k_85_end_mask_0, squeeze_mask = k_85_squeeze_mask_0, x = qkv_43)[name = tensor("k_85")]; + tensor v_43_begin_0 = const()[name = tensor("v_43_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_43_end_0 = const()[name = tensor("v_43_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_43_end_mask_0 = const()[name = tensor("v_43_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_43_squeeze_mask_0 = const()[name = tensor("v_43_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_43 = slice_by_index(begin = v_43_begin_0, end = v_43_end_0, end_mask = v_43_end_mask_0, squeeze_mask = v_43_squeeze_mask_0, x = qkv_43)[name = tensor("v_43")]; + tensor freqs_43 = const()[name = tensor("freqs_43"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210645056)))]; + tensor var_9143 = const()[name = tensor("op_9143"), val = tensor([1, 1, 1, 1])]; + tensor ts_131 = reshape(shape = var_9143, x = position21)[name = tensor("ts_131")]; + tensor var_9147 = const()[name = tensor("op_9147"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_43 = reshape(shape = var_9147, x = q_127)[name = tensor("q_complex_43")]; + tensor var_9151 = const()[name = tensor("op_9151"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_43 = reshape(shape = var_9151, x = k_85)[name = tensor("k_complex_43")]; + tensor var_9155_begin_0 = const()[name = tensor("op_9155_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9155_end_0 = const()[name = tensor("op_9155_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9155_end_mask_0 = const()[name = tensor("op_9155_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9155_squeeze_mask_0 = const()[name = tensor("op_9155_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9155 = slice_by_index(begin = var_9155_begin_0, end = var_9155_end_0, end_mask = var_9155_end_mask_0, squeeze_mask = var_9155_squeeze_mask_0, x = q_complex_43)[name = tensor("op_9155")]; + tensor var_9163_begin_0 = const()[name = tensor("op_9163_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9163_end_0 = const()[name = tensor("op_9163_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9163_end_mask_0 = const()[name = tensor("op_9163_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9163_squeeze_mask_0 = const()[name = tensor("op_9163_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9163 = slice_by_index(begin = var_9163_begin_0, end = var_9163_end_0, end_mask = var_9163_end_mask_0, squeeze_mask = var_9163_squeeze_mask_0, x = q_complex_43)[name = tensor("op_9163")]; + tensor var_9171_begin_0 = const()[name = tensor("op_9171_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9171_end_0 = const()[name = tensor("op_9171_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9171_end_mask_0 = const()[name = tensor("op_9171_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9171_squeeze_mask_0 = const()[name = tensor("op_9171_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9171 = slice_by_index(begin = var_9171_begin_0, end = var_9171_end_0, end_mask = var_9171_end_mask_0, squeeze_mask = var_9171_squeeze_mask_0, x = k_complex_43)[name = tensor("op_9171")]; + tensor var_9179_begin_0 = const()[name = tensor("op_9179_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9179_end_0 = const()[name = tensor("op_9179_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9179_end_mask_0 = const()[name = tensor("op_9179_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9179_squeeze_mask_0 = const()[name = tensor("op_9179_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9179 = slice_by_index(begin = var_9179_begin_0, end = var_9179_end_0, end_mask = var_9179_end_mask_0, squeeze_mask = var_9179_squeeze_mask_0, x = k_complex_43)[name = tensor("op_9179")]; + tensor var_9185 = mul(x = freqs_43, y = ts_131)[name = tensor("op_9185")]; + tensor rotr_43 = cos(x = var_9185)[name = tensor("rotr_43")]; + tensor roti_43 = sin(x = var_9185)[name = tensor("roti_43")]; + tensor var_9189 = mul(x = var_9155, y = rotr_43)[name = tensor("op_9189")]; + tensor var_9190 = mul(x = var_9163, y = roti_43)[name = tensor("op_9190")]; + tensor qor_85 = sub(x = var_9189, y = var_9190)[name = tensor("qor_85")]; + tensor var_9193 = mul(x = var_9155, y = roti_43)[name = tensor("op_9193")]; + tensor var_9194 = mul(x = var_9163, y = rotr_43)[name = tensor("op_9194")]; + tensor qoi_85 = add(x = var_9193, y = var_9194)[name = tensor("qoi_85")]; + tensor var_9197 = mul(x = var_9171, y = rotr_43)[name = tensor("op_9197")]; + tensor var_9198 = mul(x = var_9179, y = roti_43)[name = tensor("op_9198")]; + tensor kor_85 = sub(x = var_9197, y = var_9198)[name = tensor("kor_85")]; + tensor var_9201 = mul(x = var_9171, y = roti_43)[name = tensor("op_9201")]; + tensor var_9202 = mul(x = var_9179, y = rotr_43)[name = tensor("op_9202")]; + tensor koi_85 = add(x = var_9201, y = var_9202)[name = tensor("koi_85")]; + tensor qo_43_axis_0 = const()[name = tensor("qo_43_axis_0"), val = tensor(-1)]; + tensor qo_43 = stack(axis = qo_43_axis_0, values = (qor_85, qoi_85))[name = tensor("qo_43")]; + tensor ko_43_axis_0 = const()[name = tensor("ko_43_axis_0"), val = tensor(-1)]; + tensor ko_43 = stack(axis = ko_43_axis_0, values = (kor_85, koi_85))[name = tensor("ko_43")]; + tensor var_9231 = const()[name = tensor("op_9231"), val = tensor([1, 1, 16, 64])]; + tensor q_129 = reshape(shape = var_9231, x = qo_43)[name = tensor("q_129")]; + tensor var_9233 = const()[name = tensor("op_9233"), val = tensor([1, 1, 16, 64])]; + tensor k_87 = reshape(shape = var_9233, x = ko_43)[name = tensor("k_87")]; + tensor _inversed_9255_y_0 = const()[name = tensor("_inversed_9255_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_9255 = mul(x = ts_131, y = _inversed_9255_y_0)[name = tensor("_inversed_9255")]; + tensor var_9256 = floor(x = _inversed_9255)[name = tensor("op_9256")]; + tensor var_9257 = const()[name = tensor("op_9257"), val = tensor(0x1p+9)]; + tensor var_9258 = mul(x = var_9256, y = var_9257)[name = tensor("op_9258")]; + tensor write_indices_float_87 = sub(x = ts_131, y = var_9258)[name = tensor("write_indices_float_87")]; + tensor var_9265_dtype_0 = const()[name = tensor("op_9265_dtype_0"), val = tensor("int32")]; + tensor write_indices_43_reps_0 = const()[name = tensor("write_indices_43_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_9265 = cast(dtype = var_9265_dtype_0, x = write_indices_float_87)[name = tensor("cast_434")]; + tensor write_indices_43 = tile(reps = write_indices_43_reps_0, x = var_9265)[name = tensor("write_indices_43")]; + tensor var_9273_begin_0 = const()[name = tensor("op_9273_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9273_end_0 = const()[name = tensor("op_9273_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_9273_end_mask_0 = const()[name = tensor("op_9273_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9273_squeeze_mask_0 = const()[name = tensor("op_9273_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9273 = slice_by_index(begin = var_9273_begin_0, end = var_9273_end_0, end_mask = var_9273_end_mask_0, squeeze_mask = var_9273_squeeze_mask_0, x = cache21)[name = tensor("op_9273")]; + tensor var_9275_axis_0 = const()[name = tensor("op_9275_axis_0"), val = tensor(1)]; + tensor var_9275_mode_0 = const()[name = tensor("op_9275_mode_0"), val = tensor("update")]; + tensor var_9275_validate_indices_0 = const()[name = tensor("op_9275_validate_indices_0"), val = tensor(false)]; + tensor var_9275 = scatter_along_axis(axis = var_9275_axis_0, data = var_9273, indices = write_indices_43, mode = var_9275_mode_0, updates = k_87, validate_indices = var_9275_validate_indices_0)[name = tensor("op_9275")]; + tensor concat_149 = const()[name = tensor("concat_149"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_150 = const()[name = tensor("concat_150"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_43_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_43_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_90 = const()[name = tensor("shape_90"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_42 = const()[name = tensor("reduce_prod_42"), val = tensor(1048576)]; + tensor range_1d_42_start_0 = const()[name = tensor("range_1d_42_start_0"), val = tensor(0)]; + tensor range_1d_42_step_0 = const()[name = tensor("range_1d_42_step_0"), val = tensor(1)]; + tensor range_1d_42 = range_1d(end = reduce_prod_42, start = range_1d_42_start_0, step = range_1d_42_step_0)[name = tensor("range_1d_42")]; + tensor reshape_210 = reshape(shape = shape_90, x = range_1d_42)[name = tensor("reshape_210")]; + tensor slice_by_index_42 = slice_by_index(begin = concat_149, begin_mask = new_cache_43_internal_tensor_assign_1_begin_mask_0, end = concat_150, end_mask = new_cache_43_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_43_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_43_internal_tensor_assign_1_stride_0, x = reshape_210)[name = tensor("slice_by_index_42")]; + tensor reshape_211_shape_0 = const()[name = tensor("reshape_211_shape_0"), val = tensor([-1])]; + tensor reshape_211 = reshape(shape = reshape_211_shape_0, x = slice_by_index_42)[name = tensor("reshape_211")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([-1])]; + tensor reshape_212 = reshape(shape = reshape_212_shape_0, x = var_9275)[name = tensor("reshape_212")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([-1])]; + tensor reshape_213 = reshape(shape = reshape_213_shape_0, x = cache21)[name = tensor("reshape_213")]; + tensor scatter_42_mode_0 = const()[name = tensor("scatter_42_mode_0"), val = tensor("update")]; + tensor scatter_42_axis_0 = const()[name = tensor("scatter_42_axis_0"), val = tensor(0)]; + tensor scatter_42_validate_indices_0 = const()[name = tensor("scatter_42_validate_indices_0"), val = tensor(false)]; + tensor scatter_42 = scatter(axis = scatter_42_axis_0, data = reshape_213, indices = reshape_211, mode = scatter_42_mode_0, updates = reshape_212, validate_indices = scatter_42_validate_indices_0)[name = tensor("scatter_42")]; + tensor reshape_214 = reshape(shape = shape_90, x = scatter_42)[name = tensor("reshape_214")]; + tensor var_9283_begin_0 = const()[name = tensor("op_9283_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_9283_end_0 = const()[name = tensor("op_9283_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_9283_end_mask_0 = const()[name = tensor("op_9283_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9283_squeeze_mask_0 = const()[name = tensor("op_9283_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9283 = slice_by_index(begin = var_9283_begin_0, end = var_9283_end_0, end_mask = var_9283_end_mask_0, squeeze_mask = var_9283_squeeze_mask_0, x = reshape_214)[name = tensor("op_9283")]; + tensor var_9285_axis_0 = const()[name = tensor("op_9285_axis_0"), val = tensor(1)]; + tensor var_9285_mode_0 = const()[name = tensor("op_9285_mode_0"), val = tensor("update")]; + tensor var_9285_validate_indices_0 = const()[name = tensor("op_9285_validate_indices_0"), val = tensor(false)]; + tensor var_9285 = scatter_along_axis(axis = var_9285_axis_0, data = var_9283, indices = write_indices_43, mode = var_9285_mode_0, updates = v_43, validate_indices = var_9285_validate_indices_0)[name = tensor("op_9285")]; + tensor concat_151 = const()[name = tensor("concat_151"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_152 = const()[name = tensor("concat_152"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_43_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_43_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_43_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_43_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_91 = const()[name = tensor("shape_91"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_43 = const()[name = tensor("reduce_prod_43"), val = tensor(1048576)]; + tensor range_1d_43_start_0 = const()[name = tensor("range_1d_43_start_0"), val = tensor(0)]; + tensor range_1d_43_step_0 = const()[name = tensor("range_1d_43_step_0"), val = tensor(1)]; + tensor range_1d_43 = range_1d(end = reduce_prod_43, start = range_1d_43_start_0, step = range_1d_43_step_0)[name = tensor("range_1d_43")]; + tensor reshape_215 = reshape(shape = shape_91, x = range_1d_43)[name = tensor("reshape_215")]; + tensor slice_by_index_43 = slice_by_index(begin = concat_151, begin_mask = new_cache_43_internal_tensor_assign_2_begin_mask_0, end = concat_152, end_mask = new_cache_43_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_43_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_43_internal_tensor_assign_2_stride_0, x = reshape_215)[name = tensor("slice_by_index_43")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([-1])]; + tensor reshape_216 = reshape(shape = reshape_216_shape_0, x = slice_by_index_43)[name = tensor("reshape_216")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([-1])]; + tensor reshape_217 = reshape(shape = reshape_217_shape_0, x = var_9285)[name = tensor("reshape_217")]; + tensor reshape_218_shape_0 = const()[name = tensor("reshape_218_shape_0"), val = tensor([-1])]; + tensor reshape_218 = reshape(shape = reshape_218_shape_0, x = reshape_214)[name = tensor("reshape_218")]; + tensor scatter_43_mode_0 = const()[name = tensor("scatter_43_mode_0"), val = tensor("update")]; + tensor scatter_43_axis_0 = const()[name = tensor("scatter_43_axis_0"), val = tensor(0)]; + tensor scatter_43_validate_indices_0 = const()[name = tensor("scatter_43_validate_indices_0"), val = tensor(false)]; + tensor scatter_43 = scatter(axis = scatter_43_axis_0, data = reshape_218, indices = reshape_216, mode = scatter_43_mode_0, updates = reshape_217, validate_indices = scatter_43_validate_indices_0)[name = tensor("scatter_43")]; + tensor new_cache_43_internal_tensor_assign_2 = reshape(shape = shape_91, x = scatter_43)[name = tensor("reshape_219")]; + tensor keys_127_begin_0 = const()[name = tensor("keys_127_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_127_end_0 = const()[name = tensor("keys_127_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_127_end_mask_0 = const()[name = tensor("keys_127_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_127_squeeze_mask_0 = const()[name = tensor("keys_127_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_127 = slice_by_index(begin = keys_127_begin_0, end = keys_127_end_0, end_mask = keys_127_end_mask_0, squeeze_mask = keys_127_squeeze_mask_0, x = new_cache_43_internal_tensor_assign_2)[name = tensor("keys_127")]; + tensor values_127_begin_0 = const()[name = tensor("values_127_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_127_end_0 = const()[name = tensor("values_127_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_127_end_mask_0 = const()[name = tensor("values_127_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_127_squeeze_mask_0 = const()[name = tensor("values_127_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_127 = slice_by_index(begin = values_127_begin_0, end = values_127_end_0, end_mask = values_127_end_mask_0, squeeze_mask = values_127_squeeze_mask_0, x = new_cache_43_internal_tensor_assign_2)[name = tensor("values_127")]; + tensor var_9297 = not_equal(x = keys_127, y = keys_127)[name = tensor("op_9297")]; + tensor keys_129 = select(a = var_504, b = keys_127, cond = var_9297)[name = tensor("keys_129")]; + tensor var_9305 = not_equal(x = values_127, y = values_127)[name = tensor("op_9305")]; + tensor values_129 = select(a = var_504, b = values_127, cond = var_9305)[name = tensor("values_129")]; + tensor var_9329 = const()[name = tensor("op_9329"), val = tensor([0, 2, 1, 3])]; + tensor var_9342 = const()[name = tensor("op_9342"), val = tensor([1, 1, 1])]; + tensor var_9343 = reshape(shape = var_9342, x = position21)[name = tensor("op_9343")]; + tensor var_9360 = const()[name = tensor("op_9360"), val = tensor(0x1p+0)]; + tensor valid_len_43 = add(x = var_9343, y = var_9360)[name = tensor("valid_len_43")]; + tensor valid_mask_43 = less(x = k_positions_1_promoted, y = valid_len_43)[name = tensor("valid_mask_43")]; + tensor causal_mask_43 = less_equal(x = k_positions_1_promoted, y = var_9343)[name = tensor("causal_mask_43")]; + tensor attn_mask_85 = logical_and(x = valid_mask_43, y = causal_mask_43)[name = tensor("attn_mask_85")]; + tensor attn_mask_87_axes_0 = const()[name = tensor("attn_mask_87_axes_0"), val = tensor([1])]; + tensor attn_mask_87 = expand_dims(axes = attn_mask_87_axes_0, x = attn_mask_85)[name = tensor("attn_mask_87")]; + tensor var_9372 = const()[name = tensor("op_9372"), val = tensor([0x1.fffe5cp-4])]; + tensor var_9378_transpose_x_0 = const()[name = tensor("op_9378_transpose_x_0"), val = tensor(false)]; + tensor var_9378_transpose_y_0 = const()[name = tensor("op_9378_transpose_y_0"), val = tensor(false)]; + tensor transpose_114_perm_0 = const()[name = tensor("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = tensor("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = keys_129)[name = tensor("transpose_129")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = q_129)[name = tensor("transpose_130")]; + tensor var_9378 = matmul(transpose_x = var_9378_transpose_x_0, transpose_y = var_9378_transpose_y_0, x = transpose_114, y = transpose_115)[name = tensor("op_9378")]; + tensor attn_weights_127 = mul(x = var_9378, y = var_9372)[name = tensor("attn_weights_127")]; + tensor var_9380 = logical_not(x = attn_mask_87)[name = tensor("op_9380")]; + tensor var_9381 = const()[name = tensor("op_9381"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_129 = select(a = var_9381, b = attn_weights_127, cond = var_9380)[name = tensor("attn_weights_129")]; + tensor var_9383 = const()[name = tensor("op_9383"), val = tensor(-1)]; + tensor attn_weights_131 = softmax(axis = var_9383, x = attn_weights_129)[name = tensor("attn_weights_131")]; + tensor attn_output_43_transpose_x_0 = const()[name = tensor("attn_output_43_transpose_x_0"), val = tensor(false)]; + tensor attn_output_43_transpose_y_0 = const()[name = tensor("attn_output_43_transpose_y_0"), val = tensor(false)]; + tensor values_131 = transpose(perm = var_9329, x = values_129)[name = tensor("transpose_131")]; + tensor attn_output_43 = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = attn_weights_131, y = values_131)[name = tensor("attn_output_43")]; + tensor var_9391 = const()[name = tensor("op_9391"), val = tensor([0, 2, 1, 3])]; + tensor var_9394 = const()[name = tensor("op_9394"), val = tensor([1, 1, 1024])]; + tensor var_9392 = transpose(perm = var_9391, x = attn_output_43)[name = tensor("transpose_128")]; + tensor input_215 = reshape(shape = var_9394, x = var_9392)[name = tensor("input_215")]; + tensor attn_out_43 = linear(bias = linear_0_bias_0, weight = attn21_out_proj_weight, x = input_215)[name = tensor("linear_86")]; + tensor var_9400 = const()[name = tensor("op_9400"), val = tensor(0x1p+0)]; + tensor var_9401 = add(x = position21, y = var_9400)[name = tensor("op_9401")]; + tensor input_217 = add(x = input_213, y = attn_out_43)[name = tensor("input_217")]; + tensor var_9405 = const()[name = tensor("op_9405"), val = tensor(0x1.4f8b58p-17)]; + tensor input_219_axes_0 = const()[name = tensor("input_219_axes_0"), val = tensor([-1])]; + tensor input_219 = layer_norm(axes = input_219_axes_0, beta = norm21_2_bias, epsilon = var_9405, gamma = norm21_2_weight, x = input_217)[name = tensor("input_219")]; + tensor var_9413 = linear(bias = linear_3_bias_0, weight = linear21_1_weight, x = input_219)[name = tensor("linear_87")]; + tensor input_221_mode_0 = const()[name = tensor("input_221_mode_0"), val = tensor("EXACT")]; + tensor input_221 = gelu(mode = input_221_mode_0, x = var_9413)[name = tensor("input_221")]; + tensor ffn_out_43 = linear(bias = linear_0_bias_0, weight = linear21_2_weight, x = input_221)[name = tensor("linear_88")]; + tensor input_223 = add(x = input_217, y = ffn_out_43)[name = tensor("input_223")]; + tensor var_9422 = const()[name = tensor("op_9422"), val = tensor(0x1.4f8b58p-17)]; + tensor x_45_axes_0 = const()[name = tensor("x_45_axes_0"), val = tensor([-1])]; + tensor x_45 = layer_norm(axes = x_45_axes_0, beta = norm22_1_bias, epsilon = var_9422, gamma = norm22_1_weight, x = input_223)[name = tensor("x_45")]; + tensor var_9454 = linear(bias = linear_1_bias_0, weight = attn22_in_proj_weight, x = x_45)[name = tensor("linear_89")]; + tensor var_9458 = const()[name = tensor("op_9458"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv_45 = reshape(shape = var_9458, x = var_9454)[name = tensor("qkv_45")]; + tensor q_133_begin_0 = const()[name = tensor("q_133_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_133_end_0 = const()[name = tensor("q_133_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_133_end_mask_0 = const()[name = tensor("q_133_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_133_squeeze_mask_0 = const()[name = tensor("q_133_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_133 = slice_by_index(begin = q_133_begin_0, end = q_133_end_0, end_mask = q_133_end_mask_0, squeeze_mask = q_133_squeeze_mask_0, x = qkv_45)[name = tensor("q_133")]; + tensor k_89_begin_0 = const()[name = tensor("k_89_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_89_end_0 = const()[name = tensor("k_89_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_89_end_mask_0 = const()[name = tensor("k_89_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_89_squeeze_mask_0 = const()[name = tensor("k_89_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_89 = slice_by_index(begin = k_89_begin_0, end = k_89_end_0, end_mask = k_89_end_mask_0, squeeze_mask = k_89_squeeze_mask_0, x = qkv_45)[name = tensor("k_89")]; + tensor v_45_begin_0 = const()[name = tensor("v_45_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_45_end_0 = const()[name = tensor("v_45_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_45_end_mask_0 = const()[name = tensor("v_45_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_45_squeeze_mask_0 = const()[name = tensor("v_45_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v_45 = slice_by_index(begin = v_45_begin_0, end = v_45_end_0, end_mask = v_45_end_mask_0, squeeze_mask = v_45_squeeze_mask_0, x = qkv_45)[name = tensor("v_45")]; + tensor freqs_45 = const()[name = tensor("freqs_45"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210645248)))]; + tensor var_9562 = const()[name = tensor("op_9562"), val = tensor([1, 1, 1, 1])]; + tensor ts_137 = reshape(shape = var_9562, x = position22)[name = tensor("ts_137")]; + tensor var_9566 = const()[name = tensor("op_9566"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex_45 = reshape(shape = var_9566, x = q_133)[name = tensor("q_complex_45")]; + tensor var_9570 = const()[name = tensor("op_9570"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex_45 = reshape(shape = var_9570, x = k_89)[name = tensor("k_complex_45")]; + tensor var_9574_begin_0 = const()[name = tensor("op_9574_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9574_end_0 = const()[name = tensor("op_9574_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9574_end_mask_0 = const()[name = tensor("op_9574_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9574_squeeze_mask_0 = const()[name = tensor("op_9574_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9574 = slice_by_index(begin = var_9574_begin_0, end = var_9574_end_0, end_mask = var_9574_end_mask_0, squeeze_mask = var_9574_squeeze_mask_0, x = q_complex_45)[name = tensor("op_9574")]; + tensor var_9582_begin_0 = const()[name = tensor("op_9582_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9582_end_0 = const()[name = tensor("op_9582_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9582_end_mask_0 = const()[name = tensor("op_9582_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9582_squeeze_mask_0 = const()[name = tensor("op_9582_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9582 = slice_by_index(begin = var_9582_begin_0, end = var_9582_end_0, end_mask = var_9582_end_mask_0, squeeze_mask = var_9582_squeeze_mask_0, x = q_complex_45)[name = tensor("op_9582")]; + tensor var_9590_begin_0 = const()[name = tensor("op_9590_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9590_end_0 = const()[name = tensor("op_9590_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9590_end_mask_0 = const()[name = tensor("op_9590_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9590_squeeze_mask_0 = const()[name = tensor("op_9590_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9590 = slice_by_index(begin = var_9590_begin_0, end = var_9590_end_0, end_mask = var_9590_end_mask_0, squeeze_mask = var_9590_squeeze_mask_0, x = k_complex_45)[name = tensor("op_9590")]; + tensor var_9598_begin_0 = const()[name = tensor("op_9598_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_9598_end_0 = const()[name = tensor("op_9598_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_9598_end_mask_0 = const()[name = tensor("op_9598_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9598_squeeze_mask_0 = const()[name = tensor("op_9598_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9598 = slice_by_index(begin = var_9598_begin_0, end = var_9598_end_0, end_mask = var_9598_end_mask_0, squeeze_mask = var_9598_squeeze_mask_0, x = k_complex_45)[name = tensor("op_9598")]; + tensor var_9604 = mul(x = freqs_45, y = ts_137)[name = tensor("op_9604")]; + tensor rotr_45 = cos(x = var_9604)[name = tensor("rotr_45")]; + tensor roti_45 = sin(x = var_9604)[name = tensor("roti_45")]; + tensor var_9608 = mul(x = var_9574, y = rotr_45)[name = tensor("op_9608")]; + tensor var_9609 = mul(x = var_9582, y = roti_45)[name = tensor("op_9609")]; + tensor qor_89 = sub(x = var_9608, y = var_9609)[name = tensor("qor_89")]; + tensor var_9612 = mul(x = var_9574, y = roti_45)[name = tensor("op_9612")]; + tensor var_9613 = mul(x = var_9582, y = rotr_45)[name = tensor("op_9613")]; + tensor qoi_89 = add(x = var_9612, y = var_9613)[name = tensor("qoi_89")]; + tensor var_9616 = mul(x = var_9590, y = rotr_45)[name = tensor("op_9616")]; + tensor var_9617 = mul(x = var_9598, y = roti_45)[name = tensor("op_9617")]; + tensor kor_89 = sub(x = var_9616, y = var_9617)[name = tensor("kor_89")]; + tensor var_9620 = mul(x = var_9590, y = roti_45)[name = tensor("op_9620")]; + tensor var_9621 = mul(x = var_9598, y = rotr_45)[name = tensor("op_9621")]; + tensor koi_89 = add(x = var_9620, y = var_9621)[name = tensor("koi_89")]; + tensor qo_45_axis_0 = const()[name = tensor("qo_45_axis_0"), val = tensor(-1)]; + tensor qo_45 = stack(axis = qo_45_axis_0, values = (qor_89, qoi_89))[name = tensor("qo_45")]; + tensor ko_45_axis_0 = const()[name = tensor("ko_45_axis_0"), val = tensor(-1)]; + tensor ko_45 = stack(axis = ko_45_axis_0, values = (kor_89, koi_89))[name = tensor("ko_45")]; + tensor var_9650 = const()[name = tensor("op_9650"), val = tensor([1, 1, 16, 64])]; + tensor q_135 = reshape(shape = var_9650, x = qo_45)[name = tensor("q_135")]; + tensor var_9652 = const()[name = tensor("op_9652"), val = tensor([1, 1, 16, 64])]; + tensor k_91 = reshape(shape = var_9652, x = ko_45)[name = tensor("k_91")]; + tensor _inversed_9674_y_0 = const()[name = tensor("_inversed_9674_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_9674 = mul(x = ts_137, y = _inversed_9674_y_0)[name = tensor("_inversed_9674")]; + tensor var_9675 = floor(x = _inversed_9674)[name = tensor("op_9675")]; + tensor var_9676 = const()[name = tensor("op_9676"), val = tensor(0x1p+9)]; + tensor var_9677 = mul(x = var_9675, y = var_9676)[name = tensor("op_9677")]; + tensor write_indices_float_91 = sub(x = ts_137, y = var_9677)[name = tensor("write_indices_float_91")]; + tensor var_9684_dtype_0 = const()[name = tensor("op_9684_dtype_0"), val = tensor("int32")]; + tensor write_indices_45_reps_0 = const()[name = tensor("write_indices_45_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_9684 = cast(dtype = var_9684_dtype_0, x = write_indices_float_91)[name = tensor("cast_433")]; + tensor write_indices_45 = tile(reps = write_indices_45_reps_0, x = var_9684)[name = tensor("write_indices_45")]; + tensor var_9692_begin_0 = const()[name = tensor("op_9692_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9692_end_0 = const()[name = tensor("op_9692_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_9692_end_mask_0 = const()[name = tensor("op_9692_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9692_squeeze_mask_0 = const()[name = tensor("op_9692_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9692 = slice_by_index(begin = var_9692_begin_0, end = var_9692_end_0, end_mask = var_9692_end_mask_0, squeeze_mask = var_9692_squeeze_mask_0, x = cache22)[name = tensor("op_9692")]; + tensor var_9694_axis_0 = const()[name = tensor("op_9694_axis_0"), val = tensor(1)]; + tensor var_9694_mode_0 = const()[name = tensor("op_9694_mode_0"), val = tensor("update")]; + tensor var_9694_validate_indices_0 = const()[name = tensor("op_9694_validate_indices_0"), val = tensor(false)]; + tensor var_9694 = scatter_along_axis(axis = var_9694_axis_0, data = var_9692, indices = write_indices_45, mode = var_9694_mode_0, updates = k_91, validate_indices = var_9694_validate_indices_0)[name = tensor("op_9694")]; + tensor concat_156 = const()[name = tensor("concat_156"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_157 = const()[name = tensor("concat_157"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_45_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_45_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_92 = const()[name = tensor("shape_92"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_44 = const()[name = tensor("reduce_prod_44"), val = tensor(1048576)]; + tensor range_1d_44_start_0 = const()[name = tensor("range_1d_44_start_0"), val = tensor(0)]; + tensor range_1d_44_step_0 = const()[name = tensor("range_1d_44_step_0"), val = tensor(1)]; + tensor range_1d_44 = range_1d(end = reduce_prod_44, start = range_1d_44_start_0, step = range_1d_44_step_0)[name = tensor("range_1d_44")]; + tensor reshape_220 = reshape(shape = shape_92, x = range_1d_44)[name = tensor("reshape_220")]; + tensor slice_by_index_44 = slice_by_index(begin = concat_156, begin_mask = new_cache_45_internal_tensor_assign_1_begin_mask_0, end = concat_157, end_mask = new_cache_45_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_45_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_45_internal_tensor_assign_1_stride_0, x = reshape_220)[name = tensor("slice_by_index_44")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([-1])]; + tensor reshape_221 = reshape(shape = reshape_221_shape_0, x = slice_by_index_44)[name = tensor("reshape_221")]; + tensor reshape_222_shape_0 = const()[name = tensor("reshape_222_shape_0"), val = tensor([-1])]; + tensor reshape_222 = reshape(shape = reshape_222_shape_0, x = var_9694)[name = tensor("reshape_222")]; + tensor reshape_223_shape_0 = const()[name = tensor("reshape_223_shape_0"), val = tensor([-1])]; + tensor reshape_223 = reshape(shape = reshape_223_shape_0, x = cache22)[name = tensor("reshape_223")]; + tensor scatter_44_mode_0 = const()[name = tensor("scatter_44_mode_0"), val = tensor("update")]; + tensor scatter_44_axis_0 = const()[name = tensor("scatter_44_axis_0"), val = tensor(0)]; + tensor scatter_44_validate_indices_0 = const()[name = tensor("scatter_44_validate_indices_0"), val = tensor(false)]; + tensor scatter_44 = scatter(axis = scatter_44_axis_0, data = reshape_223, indices = reshape_221, mode = scatter_44_mode_0, updates = reshape_222, validate_indices = scatter_44_validate_indices_0)[name = tensor("scatter_44")]; + tensor reshape_224 = reshape(shape = shape_92, x = scatter_44)[name = tensor("reshape_224")]; + tensor var_9702_begin_0 = const()[name = tensor("op_9702_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_9702_end_0 = const()[name = tensor("op_9702_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_9702_end_mask_0 = const()[name = tensor("op_9702_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_9702_squeeze_mask_0 = const()[name = tensor("op_9702_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_9702 = slice_by_index(begin = var_9702_begin_0, end = var_9702_end_0, end_mask = var_9702_end_mask_0, squeeze_mask = var_9702_squeeze_mask_0, x = reshape_224)[name = tensor("op_9702")]; + tensor var_9704_axis_0 = const()[name = tensor("op_9704_axis_0"), val = tensor(1)]; + tensor var_9704_mode_0 = const()[name = tensor("op_9704_mode_0"), val = tensor("update")]; + tensor var_9704_validate_indices_0 = const()[name = tensor("op_9704_validate_indices_0"), val = tensor(false)]; + tensor var_9704 = scatter_along_axis(axis = var_9704_axis_0, data = var_9702, indices = write_indices_45, mode = var_9704_mode_0, updates = v_45, validate_indices = var_9704_validate_indices_0)[name = tensor("op_9704")]; + tensor concat_158 = const()[name = tensor("concat_158"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_159 = const()[name = tensor("concat_159"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_45_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_45_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_45_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_45_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_93 = const()[name = tensor("shape_93"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_45 = const()[name = tensor("reduce_prod_45"), val = tensor(1048576)]; + tensor range_1d_45_start_0 = const()[name = tensor("range_1d_45_start_0"), val = tensor(0)]; + tensor range_1d_45_step_0 = const()[name = tensor("range_1d_45_step_0"), val = tensor(1)]; + tensor range_1d_45 = range_1d(end = reduce_prod_45, start = range_1d_45_start_0, step = range_1d_45_step_0)[name = tensor("range_1d_45")]; + tensor reshape_225 = reshape(shape = shape_93, x = range_1d_45)[name = tensor("reshape_225")]; + tensor slice_by_index_45 = slice_by_index(begin = concat_158, begin_mask = new_cache_45_internal_tensor_assign_2_begin_mask_0, end = concat_159, end_mask = new_cache_45_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_45_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_45_internal_tensor_assign_2_stride_0, x = reshape_225)[name = tensor("slice_by_index_45")]; + tensor reshape_226_shape_0 = const()[name = tensor("reshape_226_shape_0"), val = tensor([-1])]; + tensor reshape_226 = reshape(shape = reshape_226_shape_0, x = slice_by_index_45)[name = tensor("reshape_226")]; + tensor reshape_227_shape_0 = const()[name = tensor("reshape_227_shape_0"), val = tensor([-1])]; + tensor reshape_227 = reshape(shape = reshape_227_shape_0, x = var_9704)[name = tensor("reshape_227")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([-1])]; + tensor reshape_228 = reshape(shape = reshape_228_shape_0, x = reshape_224)[name = tensor("reshape_228")]; + tensor scatter_45_mode_0 = const()[name = tensor("scatter_45_mode_0"), val = tensor("update")]; + tensor scatter_45_axis_0 = const()[name = tensor("scatter_45_axis_0"), val = tensor(0)]; + tensor scatter_45_validate_indices_0 = const()[name = tensor("scatter_45_validate_indices_0"), val = tensor(false)]; + tensor scatter_45 = scatter(axis = scatter_45_axis_0, data = reshape_228, indices = reshape_226, mode = scatter_45_mode_0, updates = reshape_227, validate_indices = scatter_45_validate_indices_0)[name = tensor("scatter_45")]; + tensor new_cache_45_internal_tensor_assign_2 = reshape(shape = shape_93, x = scatter_45)[name = tensor("reshape_229")]; + tensor keys_133_begin_0 = const()[name = tensor("keys_133_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_133_end_0 = const()[name = tensor("keys_133_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_133_end_mask_0 = const()[name = tensor("keys_133_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_133_squeeze_mask_0 = const()[name = tensor("keys_133_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_133 = slice_by_index(begin = keys_133_begin_0, end = keys_133_end_0, end_mask = keys_133_end_mask_0, squeeze_mask = keys_133_squeeze_mask_0, x = new_cache_45_internal_tensor_assign_2)[name = tensor("keys_133")]; + tensor values_133_begin_0 = const()[name = tensor("values_133_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_133_end_0 = const()[name = tensor("values_133_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_133_end_mask_0 = const()[name = tensor("values_133_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_133_squeeze_mask_0 = const()[name = tensor("values_133_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_133 = slice_by_index(begin = values_133_begin_0, end = values_133_end_0, end_mask = values_133_end_mask_0, squeeze_mask = values_133_squeeze_mask_0, x = new_cache_45_internal_tensor_assign_2)[name = tensor("values_133")]; + tensor var_9716 = not_equal(x = keys_133, y = keys_133)[name = tensor("op_9716")]; + tensor keys_135 = select(a = var_504, b = keys_133, cond = var_9716)[name = tensor("keys_135")]; + tensor var_9724 = not_equal(x = values_133, y = values_133)[name = tensor("op_9724")]; + tensor values_135 = select(a = var_504, b = values_133, cond = var_9724)[name = tensor("values_135")]; + tensor var_9748 = const()[name = tensor("op_9748"), val = tensor([0, 2, 1, 3])]; + tensor var_9761 = const()[name = tensor("op_9761"), val = tensor([1, 1, 1])]; + tensor var_9762 = reshape(shape = var_9761, x = position22)[name = tensor("op_9762")]; + tensor var_9779 = const()[name = tensor("op_9779"), val = tensor(0x1p+0)]; + tensor valid_len_45 = add(x = var_9762, y = var_9779)[name = tensor("valid_len_45")]; + tensor valid_mask_45 = less(x = k_positions_1_promoted, y = valid_len_45)[name = tensor("valid_mask_45")]; + tensor causal_mask_45 = less_equal(x = k_positions_1_promoted, y = var_9762)[name = tensor("causal_mask_45")]; + tensor attn_mask_89 = logical_and(x = valid_mask_45, y = causal_mask_45)[name = tensor("attn_mask_89")]; + tensor attn_mask_91_axes_0 = const()[name = tensor("attn_mask_91_axes_0"), val = tensor([1])]; + tensor attn_mask_91 = expand_dims(axes = attn_mask_91_axes_0, x = attn_mask_89)[name = tensor("attn_mask_91")]; + tensor var_9791 = const()[name = tensor("op_9791"), val = tensor([0x1.fffe5cp-4])]; + tensor var_9797_transpose_x_0 = const()[name = tensor("op_9797_transpose_x_0"), val = tensor(false)]; + tensor var_9797_transpose_y_0 = const()[name = tensor("op_9797_transpose_y_0"), val = tensor(false)]; + tensor transpose_116_perm_0 = const()[name = tensor("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = tensor("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = keys_135)[name = tensor("transpose_125")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = q_135)[name = tensor("transpose_126")]; + tensor var_9797 = matmul(transpose_x = var_9797_transpose_x_0, transpose_y = var_9797_transpose_y_0, x = transpose_116, y = transpose_117)[name = tensor("op_9797")]; + tensor attn_weights_133 = mul(x = var_9797, y = var_9791)[name = tensor("attn_weights_133")]; + tensor var_9799 = logical_not(x = attn_mask_91)[name = tensor("op_9799")]; + tensor var_9800 = const()[name = tensor("op_9800"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_135 = select(a = var_9800, b = attn_weights_133, cond = var_9799)[name = tensor("attn_weights_135")]; + tensor var_9802 = const()[name = tensor("op_9802"), val = tensor(-1)]; + tensor attn_weights_137 = softmax(axis = var_9802, x = attn_weights_135)[name = tensor("attn_weights_137")]; + tensor attn_output_45_transpose_x_0 = const()[name = tensor("attn_output_45_transpose_x_0"), val = tensor(false)]; + tensor attn_output_45_transpose_y_0 = const()[name = tensor("attn_output_45_transpose_y_0"), val = tensor(false)]; + tensor values_137 = transpose(perm = var_9748, x = values_135)[name = tensor("transpose_127")]; + tensor attn_output_45 = matmul(transpose_x = attn_output_45_transpose_x_0, transpose_y = attn_output_45_transpose_y_0, x = attn_weights_137, y = values_137)[name = tensor("attn_output_45")]; + tensor var_9810 = const()[name = tensor("op_9810"), val = tensor([0, 2, 1, 3])]; + tensor var_9813 = const()[name = tensor("op_9813"), val = tensor([1, 1, 1024])]; + tensor var_9811 = transpose(perm = var_9810, x = attn_output_45)[name = tensor("transpose_124")]; + tensor input_225 = reshape(shape = var_9813, x = var_9811)[name = tensor("input_225")]; + tensor attn_out_45 = linear(bias = linear_0_bias_0, weight = attn22_out_proj_weight, x = input_225)[name = tensor("linear_90")]; + tensor var_9819 = const()[name = tensor("op_9819"), val = tensor(0x1p+0)]; + tensor var_9820 = add(x = position22, y = var_9819)[name = tensor("op_9820")]; + tensor input_227 = add(x = input_223, y = attn_out_45)[name = tensor("input_227")]; + tensor var_9824 = const()[name = tensor("op_9824"), val = tensor(0x1.4f8b58p-17)]; + tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; + tensor input_229 = layer_norm(axes = input_229_axes_0, beta = norm22_2_bias, epsilon = var_9824, gamma = norm22_2_weight, x = input_227)[name = tensor("input_229")]; + tensor var_9832 = linear(bias = linear_3_bias_0, weight = linear22_1_weight, x = input_229)[name = tensor("linear_91")]; + tensor input_231_mode_0 = const()[name = tensor("input_231_mode_0"), val = tensor("EXACT")]; + tensor input_231 = gelu(mode = input_231_mode_0, x = var_9832)[name = tensor("input_231")]; + tensor ffn_out_45 = linear(bias = linear_0_bias_0, weight = linear22_2_weight, x = input_231)[name = tensor("linear_92")]; + tensor input_233 = add(x = input_227, y = ffn_out_45)[name = tensor("input_233")]; + tensor var_9841 = const()[name = tensor("op_9841"), val = tensor(0x1.4f8b58p-17)]; + tensor x_axes_0 = const()[name = tensor("x_axes_0"), val = tensor([-1])]; + tensor x = layer_norm(axes = x_axes_0, beta = norm23_1_bias, epsilon = var_9841, gamma = norm23_1_weight, x = input_233)[name = tensor("x")]; + tensor var_9873 = linear(bias = linear_1_bias_0, weight = attn23_in_proj_weight, x = x)[name = tensor("linear_93")]; + tensor var_9877 = const()[name = tensor("op_9877"), val = tensor([1, 1, 3, 16, 64])]; + tensor qkv = reshape(shape = var_9877, x = var_9873)[name = tensor("qkv")]; + tensor q_139_begin_0 = const()[name = tensor("q_139_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor q_139_end_0 = const()[name = tensor("q_139_end_0"), val = tensor([1, 1, 1, 16, 64])]; + tensor q_139_end_mask_0 = const()[name = tensor("q_139_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor q_139_squeeze_mask_0 = const()[name = tensor("q_139_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor q_139 = slice_by_index(begin = q_139_begin_0, end = q_139_end_0, end_mask = q_139_end_mask_0, squeeze_mask = q_139_squeeze_mask_0, x = qkv)[name = tensor("q_139")]; + tensor k_93_begin_0 = const()[name = tensor("k_93_begin_0"), val = tensor([0, 0, 1, 0, 0])]; + tensor k_93_end_0 = const()[name = tensor("k_93_end_0"), val = tensor([1, 1, 2, 16, 64])]; + tensor k_93_end_mask_0 = const()[name = tensor("k_93_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor k_93_squeeze_mask_0 = const()[name = tensor("k_93_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor k_93 = slice_by_index(begin = k_93_begin_0, end = k_93_end_0, end_mask = k_93_end_mask_0, squeeze_mask = k_93_squeeze_mask_0, x = qkv)[name = tensor("k_93")]; + tensor v_begin_0 = const()[name = tensor("v_begin_0"), val = tensor([0, 0, 2, 0, 0])]; + tensor v_end_0 = const()[name = tensor("v_end_0"), val = tensor([1, 1, 3, 16, 64])]; + tensor v_end_mask_0 = const()[name = tensor("v_end_mask_0"), val = tensor([true, true, false, true, true])]; + tensor v_squeeze_mask_0 = const()[name = tensor("v_squeeze_mask_0"), val = tensor([false, false, true, false, false])]; + tensor v = slice_by_index(begin = v_begin_0, end = v_end_0, end_mask = v_end_mask_0, squeeze_mask = v_squeeze_mask_0, x = qkv)[name = tensor("v")]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1210645440)))]; + tensor var_9981 = const()[name = tensor("op_9981"), val = tensor([1, 1, 1, 1])]; + tensor ts = reshape(shape = var_9981, x = position23)[name = tensor("ts")]; + tensor var_9985 = const()[name = tensor("op_9985"), val = tensor([1, 1, 16, 32, 2])]; + tensor q_complex = reshape(shape = var_9985, x = q_139)[name = tensor("q_complex")]; + tensor var_9989 = const()[name = tensor("op_9989"), val = tensor([1, 1, 16, 32, 2])]; + tensor k_complex = reshape(shape = var_9989, x = k_93)[name = tensor("k_complex")]; + tensor var_9993_begin_0 = const()[name = tensor("op_9993_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_9993_end_0 = const()[name = tensor("op_9993_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_9993_end_mask_0 = const()[name = tensor("op_9993_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_9993_squeeze_mask_0 = const()[name = tensor("op_9993_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_9993 = slice_by_index(begin = var_9993_begin_0, end = var_9993_end_0, end_mask = var_9993_end_mask_0, squeeze_mask = var_9993_squeeze_mask_0, x = q_complex)[name = tensor("op_9993")]; + tensor var_10001_begin_0 = const()[name = tensor("op_10001_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_10001_end_0 = const()[name = tensor("op_10001_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_10001_end_mask_0 = const()[name = tensor("op_10001_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_10001_squeeze_mask_0 = const()[name = tensor("op_10001_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_10001 = slice_by_index(begin = var_10001_begin_0, end = var_10001_end_0, end_mask = var_10001_end_mask_0, squeeze_mask = var_10001_squeeze_mask_0, x = q_complex)[name = tensor("op_10001")]; + tensor var_10009_begin_0 = const()[name = tensor("op_10009_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_10009_end_0 = const()[name = tensor("op_10009_end_0"), val = tensor([1, 1, 16, 32, 1])]; + tensor var_10009_end_mask_0 = const()[name = tensor("op_10009_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_10009_squeeze_mask_0 = const()[name = tensor("op_10009_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_10009 = slice_by_index(begin = var_10009_begin_0, end = var_10009_end_0, end_mask = var_10009_end_mask_0, squeeze_mask = var_10009_squeeze_mask_0, x = k_complex)[name = tensor("op_10009")]; + tensor var_10017_begin_0 = const()[name = tensor("op_10017_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_10017_end_0 = const()[name = tensor("op_10017_end_0"), val = tensor([1, 1, 16, 32, 2])]; + tensor var_10017_end_mask_0 = const()[name = tensor("op_10017_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_10017_squeeze_mask_0 = const()[name = tensor("op_10017_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_10017 = slice_by_index(begin = var_10017_begin_0, end = var_10017_end_0, end_mask = var_10017_end_mask_0, squeeze_mask = var_10017_squeeze_mask_0, x = k_complex)[name = tensor("op_10017")]; + tensor var_10023 = mul(x = freqs, y = ts)[name = tensor("op_10023")]; + tensor rotr = cos(x = var_10023)[name = tensor("rotr")]; + tensor roti = sin(x = var_10023)[name = tensor("roti")]; + tensor var_10027 = mul(x = var_9993, y = rotr)[name = tensor("op_10027")]; + tensor var_10028 = mul(x = var_10001, y = roti)[name = tensor("op_10028")]; + tensor qor_93 = sub(x = var_10027, y = var_10028)[name = tensor("qor_93")]; + tensor var_10031 = mul(x = var_9993, y = roti)[name = tensor("op_10031")]; + tensor var_10032 = mul(x = var_10001, y = rotr)[name = tensor("op_10032")]; + tensor qoi_93 = add(x = var_10031, y = var_10032)[name = tensor("qoi_93")]; + tensor var_10035 = mul(x = var_10009, y = rotr)[name = tensor("op_10035")]; + tensor var_10036 = mul(x = var_10017, y = roti)[name = tensor("op_10036")]; + tensor kor_93 = sub(x = var_10035, y = var_10036)[name = tensor("kor_93")]; + tensor var_10039 = mul(x = var_10009, y = roti)[name = tensor("op_10039")]; + tensor var_10040 = mul(x = var_10017, y = rotr)[name = tensor("op_10040")]; + tensor koi_93 = add(x = var_10039, y = var_10040)[name = tensor("koi_93")]; + tensor qo_axis_0 = const()[name = tensor("qo_axis_0"), val = tensor(-1)]; + tensor qo = stack(axis = qo_axis_0, values = (qor_93, qoi_93))[name = tensor("qo")]; + tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; + tensor ko = stack(axis = ko_axis_0, values = (kor_93, koi_93))[name = tensor("ko")]; + tensor var_10069 = const()[name = tensor("op_10069"), val = tensor([1, 1, 16, 64])]; + tensor q_141 = reshape(shape = var_10069, x = qo)[name = tensor("q_141")]; + tensor var_10071 = const()[name = tensor("op_10071"), val = tensor([1, 1, 16, 64])]; + tensor k = reshape(shape = var_10071, x = ko)[name = tensor("k")]; + tensor _inversed_10093_y_0 = const()[name = tensor("_inversed_10093_y_0"), val = tensor(0x1p-9)]; + tensor _inversed_10093 = mul(x = ts, y = _inversed_10093_y_0)[name = tensor("_inversed_10093")]; + tensor var_10094 = floor(x = _inversed_10093)[name = tensor("op_10094")]; + tensor var_10095 = const()[name = tensor("op_10095"), val = tensor(0x1p+9)]; + tensor var_10096 = mul(x = var_10094, y = var_10095)[name = tensor("op_10096")]; + tensor write_indices_float = sub(x = ts, y = var_10096)[name = tensor("write_indices_float")]; + tensor var_10103_dtype_0 = const()[name = tensor("op_10103_dtype_0"), val = tensor("int32")]; + tensor write_indices_reps_0 = const()[name = tensor("write_indices_reps_0"), val = tensor([1, 1, 16, 64])]; + tensor var_10103 = cast(dtype = var_10103_dtype_0, x = write_indices_float)[name = tensor("cast_432")]; + tensor write_indices = tile(reps = write_indices_reps_0, x = var_10103)[name = tensor("write_indices")]; + tensor var_10111_begin_0 = const()[name = tensor("op_10111_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_10111_end_0 = const()[name = tensor("op_10111_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor var_10111_end_mask_0 = const()[name = tensor("op_10111_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_10111_squeeze_mask_0 = const()[name = tensor("op_10111_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_10111 = slice_by_index(begin = var_10111_begin_0, end = var_10111_end_0, end_mask = var_10111_end_mask_0, squeeze_mask = var_10111_squeeze_mask_0, x = cache23)[name = tensor("op_10111")]; + tensor var_10113_axis_0 = const()[name = tensor("op_10113_axis_0"), val = tensor(1)]; + tensor var_10113_mode_0 = const()[name = tensor("op_10113_mode_0"), val = tensor("update")]; + tensor var_10113_validate_indices_0 = const()[name = tensor("op_10113_validate_indices_0"), val = tensor(false)]; + tensor var_10113 = scatter_along_axis(axis = var_10113_axis_0, data = var_10111, indices = write_indices, mode = var_10113_mode_0, updates = k, validate_indices = var_10113_validate_indices_0)[name = tensor("op_10113")]; + tensor concat_163 = const()[name = tensor("concat_163"), val = tensor([0, 0, 0, 0, 0])]; + tensor concat_164 = const()[name = tensor("concat_164"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_1_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_94 = const()[name = tensor("shape_94"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_46 = const()[name = tensor("reduce_prod_46"), val = tensor(1048576)]; + tensor range_1d_46_start_0 = const()[name = tensor("range_1d_46_start_0"), val = tensor(0)]; + tensor range_1d_46_step_0 = const()[name = tensor("range_1d_46_step_0"), val = tensor(1)]; + tensor range_1d_46 = range_1d(end = reduce_prod_46, start = range_1d_46_start_0, step = range_1d_46_step_0)[name = tensor("range_1d_46")]; + tensor reshape_230 = reshape(shape = shape_94, x = range_1d_46)[name = tensor("reshape_230")]; + tensor slice_by_index_46 = slice_by_index(begin = concat_163, begin_mask = new_cache_internal_tensor_assign_1_begin_mask_0, end = concat_164, end_mask = new_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_1_squeeze_mask_0, stride = new_cache_internal_tensor_assign_1_stride_0, x = reshape_230)[name = tensor("slice_by_index_46")]; + tensor reshape_231_shape_0 = const()[name = tensor("reshape_231_shape_0"), val = tensor([-1])]; + tensor reshape_231 = reshape(shape = reshape_231_shape_0, x = slice_by_index_46)[name = tensor("reshape_231")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([-1])]; + tensor reshape_232 = reshape(shape = reshape_232_shape_0, x = var_10113)[name = tensor("reshape_232")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([-1])]; + tensor reshape_233 = reshape(shape = reshape_233_shape_0, x = cache23)[name = tensor("reshape_233")]; + tensor scatter_46_mode_0 = const()[name = tensor("scatter_46_mode_0"), val = tensor("update")]; + tensor scatter_46_axis_0 = const()[name = tensor("scatter_46_axis_0"), val = tensor(0)]; + tensor scatter_46_validate_indices_0 = const()[name = tensor("scatter_46_validate_indices_0"), val = tensor(false)]; + tensor scatter_46 = scatter(axis = scatter_46_axis_0, data = reshape_233, indices = reshape_231, mode = scatter_46_mode_0, updates = reshape_232, validate_indices = scatter_46_validate_indices_0)[name = tensor("scatter_46")]; + tensor reshape_234 = reshape(shape = shape_94, x = scatter_46)[name = tensor("reshape_234")]; + tensor var_10121_begin_0 = const()[name = tensor("op_10121_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_10121_end_0 = const()[name = tensor("op_10121_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor var_10121_end_mask_0 = const()[name = tensor("op_10121_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_10121_squeeze_mask_0 = const()[name = tensor("op_10121_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_10121 = slice_by_index(begin = var_10121_begin_0, end = var_10121_end_0, end_mask = var_10121_end_mask_0, squeeze_mask = var_10121_squeeze_mask_0, x = reshape_234)[name = tensor("op_10121")]; + tensor var_10123_axis_0 = const()[name = tensor("op_10123_axis_0"), val = tensor(1)]; + tensor var_10123_mode_0 = const()[name = tensor("op_10123_mode_0"), val = tensor("update")]; + tensor var_10123_validate_indices_0 = const()[name = tensor("op_10123_validate_indices_0"), val = tensor(false)]; + tensor var_10123 = scatter_along_axis(axis = var_10123_axis_0, data = var_10121, indices = write_indices, mode = var_10123_mode_0, updates = v, validate_indices = var_10123_validate_indices_0)[name = tensor("op_10123")]; + tensor concat_165 = const()[name = tensor("concat_165"), val = tensor([1, 0, 0, 0, 0])]; + tensor concat_166 = const()[name = tensor("concat_166"), val = tensor([0, 0, 0, 0, 0])]; + tensor new_cache_internal_tensor_assign_2_stride_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1, 1])]; + tensor new_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_end_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor new_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = tensor("new_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor shape_95 = const()[name = tensor("shape_95"), val = tensor([2, 1, 512, 16, 64])]; + tensor reduce_prod_47 = const()[name = tensor("reduce_prod_47"), val = tensor(1048576)]; + tensor range_1d_47_start_0 = const()[name = tensor("range_1d_47_start_0"), val = tensor(0)]; + tensor range_1d_47_step_0 = const()[name = tensor("range_1d_47_step_0"), val = tensor(1)]; + tensor range_1d_47 = range_1d(end = reduce_prod_47, start = range_1d_47_start_0, step = range_1d_47_step_0)[name = tensor("range_1d_47")]; + tensor reshape_235 = reshape(shape = shape_95, x = range_1d_47)[name = tensor("reshape_235")]; + tensor slice_by_index_47 = slice_by_index(begin = concat_165, begin_mask = new_cache_internal_tensor_assign_2_begin_mask_0, end = concat_166, end_mask = new_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = new_cache_internal_tensor_assign_2_squeeze_mask_0, stride = new_cache_internal_tensor_assign_2_stride_0, x = reshape_235)[name = tensor("slice_by_index_47")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([-1])]; + tensor reshape_236 = reshape(shape = reshape_236_shape_0, x = slice_by_index_47)[name = tensor("reshape_236")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([-1])]; + tensor reshape_237 = reshape(shape = reshape_237_shape_0, x = var_10123)[name = tensor("reshape_237")]; + tensor reshape_238_shape_0 = const()[name = tensor("reshape_238_shape_0"), val = tensor([-1])]; + tensor reshape_238 = reshape(shape = reshape_238_shape_0, x = reshape_234)[name = tensor("reshape_238")]; + tensor scatter_47_mode_0 = const()[name = tensor("scatter_47_mode_0"), val = tensor("update")]; + tensor scatter_47_axis_0 = const()[name = tensor("scatter_47_axis_0"), val = tensor(0)]; + tensor scatter_47_validate_indices_0 = const()[name = tensor("scatter_47_validate_indices_0"), val = tensor(false)]; + tensor scatter_47 = scatter(axis = scatter_47_axis_0, data = reshape_238, indices = reshape_236, mode = scatter_47_mode_0, updates = reshape_237, validate_indices = scatter_47_validate_indices_0)[name = tensor("scatter_47")]; + tensor new_cache_internal_tensor_assign_2 = reshape(shape = shape_95, x = scatter_47)[name = tensor("reshape_239")]; + tensor keys_139_begin_0 = const()[name = tensor("keys_139_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor keys_139_end_0 = const()[name = tensor("keys_139_end_0"), val = tensor([1, 1, 512, 16, 64])]; + tensor keys_139_end_mask_0 = const()[name = tensor("keys_139_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor keys_139_squeeze_mask_0 = const()[name = tensor("keys_139_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor keys_139 = slice_by_index(begin = keys_139_begin_0, end = keys_139_end_0, end_mask = keys_139_end_mask_0, squeeze_mask = keys_139_squeeze_mask_0, x = new_cache_internal_tensor_assign_2)[name = tensor("keys_139")]; + tensor values_139_begin_0 = const()[name = tensor("values_139_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor values_139_end_0 = const()[name = tensor("values_139_end_0"), val = tensor([2, 1, 512, 16, 64])]; + tensor values_139_end_mask_0 = const()[name = tensor("values_139_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor values_139_squeeze_mask_0 = const()[name = tensor("values_139_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor values_139 = slice_by_index(begin = values_139_begin_0, end = values_139_end_0, end_mask = values_139_end_mask_0, squeeze_mask = values_139_squeeze_mask_0, x = new_cache_internal_tensor_assign_2)[name = tensor("values_139")]; + tensor var_10135 = not_equal(x = keys_139, y = keys_139)[name = tensor("op_10135")]; + tensor keys_141 = select(a = var_504, b = keys_139, cond = var_10135)[name = tensor("keys_141")]; + tensor var_10143 = not_equal(x = values_139, y = values_139)[name = tensor("op_10143")]; + tensor values_141 = select(a = var_504, b = values_139, cond = var_10143)[name = tensor("values_141")]; + tensor var_10167 = const()[name = tensor("op_10167"), val = tensor([0, 2, 1, 3])]; + tensor var_10180 = const()[name = tensor("op_10180"), val = tensor([1, 1, 1])]; + tensor var_10181 = reshape(shape = var_10180, x = position23)[name = tensor("op_10181")]; + tensor var_10198 = const()[name = tensor("op_10198"), val = tensor(0x1p+0)]; + tensor valid_len = add(x = var_10181, y = var_10198)[name = tensor("valid_len")]; + tensor valid_mask = less(x = k_positions_1_promoted, y = valid_len)[name = tensor("valid_mask")]; + tensor causal_mask = less_equal(x = k_positions_1_promoted, y = var_10181)[name = tensor("causal_mask")]; + tensor attn_mask_93 = logical_and(x = valid_mask, y = causal_mask)[name = tensor("attn_mask_93")]; + tensor attn_mask_axes_0 = const()[name = tensor("attn_mask_axes_0"), val = tensor([1])]; + tensor attn_mask = expand_dims(axes = attn_mask_axes_0, x = attn_mask_93)[name = tensor("attn_mask")]; + tensor var_10210 = const()[name = tensor("op_10210"), val = tensor([0x1.fffe5cp-4])]; + tensor var_10216_transpose_x_0 = const()[name = tensor("op_10216_transpose_x_0"), val = tensor(false)]; + tensor var_10216_transpose_y_0 = const()[name = tensor("op_10216_transpose_y_0"), val = tensor(false)]; + tensor transpose_118_perm_0 = const()[name = tensor("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = tensor("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = keys_141)[name = tensor("transpose_121")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = q_141)[name = tensor("transpose_122")]; + tensor var_10216 = matmul(transpose_x = var_10216_transpose_x_0, transpose_y = var_10216_transpose_y_0, x = transpose_118, y = transpose_119)[name = tensor("op_10216")]; + tensor attn_weights_139 = mul(x = var_10216, y = var_10210)[name = tensor("attn_weights_139")]; + tensor var_10218 = logical_not(x = attn_mask)[name = tensor("op_10218")]; + tensor var_10219 = const()[name = tensor("op_10219"), val = tensor(-0x1.ff933cp+127)]; + tensor attn_weights_141 = select(a = var_10219, b = attn_weights_139, cond = var_10218)[name = tensor("attn_weights_141")]; + tensor var_10221 = const()[name = tensor("op_10221"), val = tensor(-1)]; + tensor attn_weights = softmax(axis = var_10221, x = attn_weights_141)[name = tensor("attn_weights")]; + tensor attn_output_transpose_x_0 = const()[name = tensor("attn_output_transpose_x_0"), val = tensor(false)]; + tensor attn_output_transpose_y_0 = const()[name = tensor("attn_output_transpose_y_0"), val = tensor(false)]; + tensor values = transpose(perm = var_10167, x = values_141)[name = tensor("transpose_123")]; + tensor attn_output = matmul(transpose_x = attn_output_transpose_x_0, transpose_y = attn_output_transpose_y_0, x = attn_weights, y = values)[name = tensor("attn_output")]; + tensor var_10229 = const()[name = tensor("op_10229"), val = tensor([0, 2, 1, 3])]; + tensor var_10232 = const()[name = tensor("op_10232"), val = tensor([1, 1, 1024])]; + tensor var_10230 = transpose(perm = var_10229, x = attn_output)[name = tensor("transpose_120")]; + tensor input_235 = reshape(shape = var_10232, x = var_10230)[name = tensor("input_235")]; + tensor attn_out = linear(bias = linear_0_bias_0, weight = attn23_out_proj_weight, x = input_235)[name = tensor("linear_94")]; + tensor var_10238 = const()[name = tensor("op_10238"), val = tensor(0x1p+0)]; + tensor var_10239 = add(x = position23, y = var_10238)[name = tensor("op_10239")]; + tensor input_237 = add(x = input_233, y = attn_out)[name = tensor("input_237")]; + tensor var_10243 = const()[name = tensor("op_10243"), val = tensor(0x1.4f8b58p-17)]; + tensor input_239_axes_0 = const()[name = tensor("input_239_axes_0"), val = tensor([-1])]; + tensor input_239 = layer_norm(axes = input_239_axes_0, beta = norm23_2_bias, epsilon = var_10243, gamma = norm23_2_weight, x = input_237)[name = tensor("input_239")]; + tensor var_10251 = linear(bias = linear_3_bias_0, weight = linear23_1_weight, x = input_239)[name = tensor("linear_95")]; + tensor input_241_mode_0 = const()[name = tensor("input_241_mode_0"), val = tensor("EXACT")]; + tensor input_241 = gelu(mode = input_241_mode_0, x = var_10251)[name = tensor("input_241")]; + tensor ffn_out = linear(bias = linear_0_bias_0, weight = linear23_2_weight, x = input_241)[name = tensor("linear_96")]; + tensor input_243 = add(x = input_237, y = ffn_out)[name = tensor("input_243")]; + tensor var_10260 = const()[name = tensor("op_10260"), val = tensor(0x1.4f8b58p-17)]; + tensor input_axes_0 = const()[name = tensor("input_axes_0"), val = tensor([-1])]; + tensor input = layer_norm(axes = input_axes_0, beta = out_norm_bias, epsilon = var_10260, gamma = out_norm_weight, x = input_243)[name = tensor("input")]; + tensor var_10268 = linear(bias = out_eos_bias, weight = out_eos_weight, x = input)[name = tensor("linear_97")]; + } -> (input, var_10268, new_cache_1_internal_tensor_assign_2, var_602, new_cache_3_internal_tensor_assign_2, var_1021, new_cache_5_internal_tensor_assign_2, var_1440, new_cache_7_internal_tensor_assign_2, var_1859, new_cache_9_internal_tensor_assign_2, var_2278, new_cache_11_internal_tensor_assign_2, var_2697, new_cache_13_internal_tensor_assign_2, var_3116, new_cache_15_internal_tensor_assign_2, var_3535, new_cache_17_internal_tensor_assign_2, var_3954, new_cache_19_internal_tensor_assign_2, var_4373, new_cache_21_internal_tensor_assign_2, var_4792, new_cache_23_internal_tensor_assign_2, var_5211, new_cache_25_internal_tensor_assign_2, var_5630, new_cache_27_internal_tensor_assign_2, var_6049, new_cache_29_internal_tensor_assign_2, var_6468, new_cache_31_internal_tensor_assign_2, var_6887, new_cache_33_internal_tensor_assign_2, var_7306, new_cache_35_internal_tensor_assign_2, var_7725, new_cache_37_internal_tensor_assign_2, var_8144, new_cache_39_internal_tensor_assign_2, var_8563, new_cache_41_internal_tensor_assign_2, var_8982, new_cache_43_internal_tensor_assign_2, var_9401, new_cache_45_internal_tensor_assign_2, var_9820, new_cache_internal_tensor_assign_2, var_10239); +} \ No newline at end of file diff --git a/v2/french_24l/flowlm_step.mlmodelc/weights/weight.bin b/v2/french_24l/flowlm_step.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a2608540ca1cc0de178ee67e8ae44bac1f7554ba --- /dev/null +++ b/v2/french_24l/flowlm_step.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"generatedClassName" : "mimi_decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/v2/french_24l/mimi_decoder.mlmodelc/model.mil b/v2/french_24l/mimi_decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..c96d4668e23dbb8f1f9e9d85b199ffa213c5c6b1 --- /dev/null +++ b/v2/french_24l/mimi_decoder.mlmodelc/model.mil @@ -0,0 +1,646 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor attn0_cache, tensor attn0_offset, tensor attn1_cache, tensor attn1_offset, tensor conv0_first, tensor conv0_prev, tensor conv_final_first, tensor conv_final_prev, tensor convtr0_partial, tensor convtr1_partial, tensor convtr2_partial, tensor latent, tensor res0_conv0_first, tensor res0_conv0_prev, tensor res0_conv1_first, tensor res0_conv1_prev, tensor res1_conv0_first, tensor res1_conv0_prev, tensor res1_conv1_first, tensor res1_conv1_prev, tensor res2_conv0_first, tensor res2_conv0_prev, tensor res2_conv1_first, tensor res2_conv1_prev, tensor upsample_partial) { + tensor emb_mean = const()[name = tensor("emb_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_std = const()[name = tensor("emb_std"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256)))]; + tensor mimi_quantizer_output_proj_weight = const()[name = tensor("mimi_quantizer_output_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor mimi_upsample_convtr_convtr_weight = const()[name = tensor("mimi_upsample_convtr_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66048)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm1_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131648)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133760)))]; + tensor mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135872)))]; + tensor mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3281664)))]; + tensor mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4330304)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm2_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4332416)))]; + tensor mimi_decoder_transformer_transformer_layers_0_norm2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4334528)))]; + tensor mimi_decoder_transformer_transformer_layers_0_linear1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4336640)))]; + tensor mimi_decoder_transformer_transformer_layers_0_linear2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8531008)))]; + tensor mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12725376)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm1_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12727488)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12729600)))]; + tensor mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12731712)))]; + tensor mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15877504)))]; + tensor mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16926144)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm2_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16928256)))]; + tensor mimi_decoder_transformer_transformer_layers_1_norm2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16930368)))]; + tensor mimi_decoder_transformer_transformer_layers_1_linear1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16932480)))]; + tensor mimi_decoder_transformer_transformer_layers_1_linear2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21126848)))]; + tensor mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25321216)))]; + tensor mimi_decoder_model_0_conv_bias = const()[name = tensor("mimi_decoder_model_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25323328)))]; + tensor mimi_decoder_model_0_conv_weight = const()[name = tensor("mimi_decoder_model_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25325440)))]; + tensor mimi_decoder_model_2_convtr_bias = const()[name = tensor("mimi_decoder_model_2_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32665536)))]; + tensor mimi_decoder_model_2_convtr_weight = const()[name = tensor("mimi_decoder_model_2_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32666624)))]; + tensor mimi_decoder_model_3_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_3_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958144)))]; + tensor mimi_decoder_model_3_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_3_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958720)))]; + tensor mimi_decoder_model_3_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_3_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39352000)))]; + tensor mimi_decoder_model_3_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_3_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39353088)))]; + tensor mimi_decoder_model_5_convtr_bias = const()[name = tensor("mimi_decoder_model_5_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484224)))]; + tensor mimi_decoder_model_5_convtr_weight = const()[name = tensor("mimi_decoder_model_5_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484800)))]; + tensor mimi_decoder_model_6_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_6_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40795584)))]; + tensor mimi_decoder_model_6_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_6_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40795904)))]; + tensor mimi_decoder_model_6_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_6_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40894272)))]; + tensor mimi_decoder_model_6_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_6_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40894848)))]; + tensor mimi_decoder_model_8_convtr_bias = const()[name = tensor("mimi_decoder_model_8_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40927680)))]; + tensor mimi_decoder_model_8_convtr_weight = const()[name = tensor("mimi_decoder_model_8_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40928000)))]; + tensor mimi_decoder_model_9_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_9_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41190208)))]; + tensor mimi_decoder_model_9_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_9_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41190400)))]; + tensor mimi_decoder_model_9_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_9_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41215040)))]; + tensor mimi_decoder_model_9_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_9_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41215360)))]; + tensor mimi_decoder_model_11_conv_bias = const()[name = tensor("mimi_decoder_model_11_conv_bias"), val = tensor([0x1.16p-13])]; + tensor mimi_decoder_model_11_conv_weight = const()[name = tensor("mimi_decoder_model_11_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41223616)))]; + tensor var_38 = mul(x = latent, y = emb_std)[name = tensor("op_38")]; + tensor denorm = add(x = var_38, y = emb_mean)[name = tensor("denorm")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = denorm)[name = tensor("input_1")]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("valid")]; + tensor x_1_strides_0 = const()[name = tensor("x_1_strides_0"), val = tensor([1])]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0])]; + tensor x_1_dilations_0 = const()[name = tensor("x_1_dilations_0"), val = tensor([1])]; + tensor x_1_groups_0 = const()[name = tensor("x_1_groups_0"), val = tensor(1)]; + tensor x_1 = conv(dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = mimi_quantizer_output_proj_weight, x = input_1)[name = tensor("x_1")]; + tensor var_62 = const()[name = tensor("op_62"), val = tensor(-1)]; + tensor y_1_pad_type_0 = const()[name = tensor("y_1_pad_type_0"), val = tensor("valid")]; + tensor y_1_strides_0 = const()[name = tensor("y_1_strides_0"), val = tensor([16])]; + tensor y_1_groups_0 = const()[name = tensor("y_1_groups_0"), val = tensor(512)]; + tensor y_1_pad_0 = const()[name = tensor("y_1_pad_0"), val = tensor([0, 0])]; + tensor y_1_dilations_0 = const()[name = tensor("y_1_dilations_0"), val = tensor([1])]; + tensor y_1_has_output_shape_output_shape_0 = const()[name = tensor("y_1_has_output_shape_output_shape_0"), val = tensor([1, 512, 32])]; + tensor y_1_has_output_shape = conv_transpose(dilations = y_1_dilations_0, groups = y_1_groups_0, output_shape = y_1_has_output_shape_output_shape_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = mimi_upsample_convtr_convtr_weight, x = x_1)[name = tensor("y_1_has_output_shape")]; + tensor var_72_begin_0 = const()[name = tensor("op_72_begin_0"), val = tensor([0, 0, 0])]; + tensor var_72_end_0 = const()[name = tensor("op_72_end_0"), val = tensor([1, 512, 16])]; + tensor var_72_end_mask_0 = const()[name = tensor("op_72_end_mask_0"), val = tensor([true, true, false])]; + tensor var_72 = slice_by_index(begin = var_72_begin_0, end = var_72_end_0, end_mask = var_72_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_72")]; + tensor var_73 = add(x = var_72, y = upsample_partial)[name = tensor("op_73")]; + tensor var_74_begin_0 = const()[name = tensor("op_74_begin_0"), val = tensor([0, 0, 16])]; + tensor var_74_end_0 = const()[name = tensor("op_74_end_0"), val = tensor([1, 512, 32])]; + tensor var_74_end_mask_0 = const()[name = tensor("op_74_end_mask_0"), val = tensor([true, true, true])]; + tensor var_74 = slice_by_index(begin = var_74_begin_0, end = var_74_end_0, end_mask = var_74_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_74")]; + tensor y_3_interleave_0 = const()[name = tensor("y_3_interleave_0"), val = tensor(false)]; + tensor y_3 = concat(axis = var_62, interleave = y_3_interleave_0, values = (var_73, var_74))[name = tensor("y_3")]; + tensor var_77_begin_0 = const()[name = tensor("op_77_begin_0"), val = tensor([0, 0, 16])]; + tensor var_77_end_0 = const()[name = tensor("op_77_end_0"), val = tensor([1, 512, 32])]; + tensor var_77_end_mask_0 = const()[name = tensor("op_77_end_mask_0"), val = tensor([true, true, true])]; + tensor var_77 = slice_by_index(begin = var_77_begin_0, end = var_77_end_0, end_mask = var_77_end_mask_0, x = y_3)[name = tensor("op_77")]; + tensor x_3_begin_0 = const()[name = tensor("x_3_begin_0"), val = tensor([0, 0, 0])]; + tensor x_3_end_0 = const()[name = tensor("x_3_end_0"), val = tensor([1, 512, 16])]; + tensor x_3_end_mask_0 = const()[name = tensor("x_3_end_mask_0"), val = tensor([true, true, false])]; + tensor x_3 = slice_by_index(begin = x_3_begin_0, end = x_3_end_0, end_mask = x_3_end_mask_0, x = y_3)[name = tensor("x_3")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(0)]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor(-1)]; + tensor var_100 = const()[name = tensor("op_100"), val = tensor(-0x1.ff933cp+127)]; + tensor var_102 = const()[name = tensor("op_102"), val = tensor(0x1.4f8b58p-17)]; + tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; + tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; + tensor input_3 = transpose(perm = input_3_perm_0, x = x_3)[name = tensor("transpose_19")]; + tensor query_1 = layer_norm(axes = query_1_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm1_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_0_norm1_weight, x = input_3)[name = tensor("query_1")]; + tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41224448)))]; + tensor projected_1 = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight, x = query_1)[name = tensor("linear_0")]; + tensor var_130 = const()[name = tensor("op_130"), val = tensor([1, 16, 3, 8, 64])]; + tensor packed_1 = reshape(shape = var_130, x = projected_1)[name = tensor("packed_1")]; + tensor var_132_split_sizes_0 = const()[name = tensor("op_132_split_sizes_0"), val = tensor([1, 1, 1])]; + tensor var_132_axis_0 = const()[name = tensor("op_132_axis_0"), val = tensor(2)]; + tensor var_132_0, tensor var_132_1, tensor var_132_2 = split(axis = var_132_axis_0, split_sizes = var_132_split_sizes_0, x = packed_1)[name = tensor("op_132")]; + tensor squeeze_0_axes_0 = const()[name = tensor("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = var_132_0)[name = tensor("squeeze_0")]; + tensor squeeze_1_axes_0 = const()[name = tensor("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = var_132_1)[name = tensor("squeeze_1")]; + tensor squeeze_2_axes_0 = const()[name = tensor("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = var_132_2)[name = tensor("squeeze_2")]; + tensor offset_3_begin_0 = const()[name = tensor("offset_3_begin_0"), val = tensor([0])]; + tensor offset_3_end_0 = const()[name = tensor("offset_3_end_0"), val = tensor([1])]; + tensor offset_3_end_mask_0 = const()[name = tensor("offset_3_end_mask_0"), val = tensor([false])]; + tensor offset_3_squeeze_mask_0 = const()[name = tensor("offset_3_squeeze_mask_0"), val = tensor([true])]; + tensor offset_3 = slice_by_index(begin = offset_3_begin_0, end = offset_3_end_0, end_mask = offset_3_end_mask_0, squeeze_mask = offset_3_squeeze_mask_0, x = attn0_offset)[name = tensor("offset_3")]; + tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230656)))]; + tensor ts_1_promoted = const()[name = tensor("ts_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230848)))]; + tensor ts_3 = add(x = ts_1_promoted, y = offset_3)[name = tensor("ts_3")]; + tensor var_148 = const()[name = tensor("op_148"), val = tensor([-1, 1, 1])]; + tensor ts_5 = reshape(shape = var_148, x = ts_3)[name = tensor("ts_5")]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 16, 8, 32, 2])]; + tensor q_3 = reshape(shape = var_150, x = squeeze_0)[name = tensor("q_3")]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 16, 8, 32, 2])]; + tensor k_3 = reshape(shape = var_152, x = squeeze_1)[name = tensor("k_3")]; + tensor var_154_begin_0 = const()[name = tensor("op_154_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_154_end_0 = const()[name = tensor("op_154_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_154_end_mask_0 = const()[name = tensor("op_154_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_154_squeeze_mask_0 = const()[name = tensor("op_154_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_154 = slice_by_index(begin = var_154_begin_0, end = var_154_end_0, end_mask = var_154_end_mask_0, squeeze_mask = var_154_squeeze_mask_0, x = q_3)[name = tensor("op_154")]; + tensor var_156_begin_0 = const()[name = tensor("op_156_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_156_end_0 = const()[name = tensor("op_156_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_156_end_mask_0 = const()[name = tensor("op_156_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_156_squeeze_mask_0 = const()[name = tensor("op_156_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_156 = slice_by_index(begin = var_156_begin_0, end = var_156_end_0, end_mask = var_156_end_mask_0, squeeze_mask = var_156_squeeze_mask_0, x = q_3)[name = tensor("op_156")]; + tensor var_158_begin_0 = const()[name = tensor("op_158_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_158_end_0 = const()[name = tensor("op_158_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_158_end_mask_0 = const()[name = tensor("op_158_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_158_squeeze_mask_0 = const()[name = tensor("op_158_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_158 = slice_by_index(begin = var_158_begin_0, end = var_158_end_0, end_mask = var_158_end_mask_0, squeeze_mask = var_158_squeeze_mask_0, x = k_3)[name = tensor("op_158")]; + tensor var_160_begin_0 = const()[name = tensor("op_160_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_160_end_0 = const()[name = tensor("op_160_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_160_end_mask_0 = const()[name = tensor("op_160_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_160_squeeze_mask_0 = const()[name = tensor("op_160_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_160 = slice_by_index(begin = var_160_begin_0, end = var_160_end_0, end_mask = var_160_end_mask_0, squeeze_mask = var_160_squeeze_mask_0, x = k_3)[name = tensor("op_160")]; + tensor var_162 = mul(x = freqs_1, y = ts_5)[name = tensor("op_162")]; + tensor rotr_1 = cos(x = var_162)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_162)[name = tensor("roti_1")]; + tensor var_166 = mul(x = var_154, y = rotr_1)[name = tensor("op_166")]; + tensor var_167 = mul(x = var_156, y = roti_1)[name = tensor("op_167")]; + tensor qor_1 = sub(x = var_166, y = var_167)[name = tensor("qor_1")]; + tensor var_169 = mul(x = var_154, y = roti_1)[name = tensor("op_169")]; + tensor var_170 = mul(x = var_156, y = rotr_1)[name = tensor("op_170")]; + tensor qoi_1 = add(x = var_169, y = var_170)[name = tensor("qoi_1")]; + tensor var_172 = mul(x = var_158, y = rotr_1)[name = tensor("op_172")]; + tensor var_173 = mul(x = var_160, y = roti_1)[name = tensor("op_173")]; + tensor kor_1 = sub(x = var_172, y = var_173)[name = tensor("kor_1")]; + tensor var_175 = mul(x = var_158, y = roti_1)[name = tensor("op_175")]; + tensor var_176 = mul(x = var_160, y = rotr_1)[name = tensor("op_176")]; + tensor koi_1 = add(x = var_175, y = var_176)[name = tensor("koi_1")]; + tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; + tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; + tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; + tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; + tensor var_186 = const()[name = tensor("op_186"), val = tensor([1, 16, 8, 64])]; + tensor q_5 = reshape(shape = var_186, x = qo_1)[name = tensor("q_5")]; + tensor var_188 = const()[name = tensor("op_188"), val = tensor([1, 16, 8, 64])]; + tensor k_5 = reshape(shape = var_188, x = ko_1)[name = tensor("k_5")]; + tensor capacity_1 = const()[name = tensor("capacity_1"), val = tensor([256])]; + tensor var_193_dtype_0 = const()[name = tensor("op_193_dtype_0"), val = tensor("int32")]; + tensor var_194 = const()[name = tensor("op_194"), val = tensor([1, 1])]; + tensor var_193 = cast(dtype = var_193_dtype_0, x = attn0_offset)[name = tensor("cast_49")]; + tensor write_base_1 = reshape(shape = var_194, x = var_193)[name = tensor("write_base_1")]; + tensor write_range_1 = const()[name = tensor("write_range_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; + tensor abs_idx_1 = add(x = write_base_1, y = write_range_1)[name = tensor("abs_idx_1")]; + tensor wrapped_1_div = floor_div(x = abs_idx_1, y = capacity_1)[name = tensor("wrapped_1_div")]; + tensor wrapped_1_div_scaled = mul(x = wrapped_1_div, y = capacity_1)[name = tensor("wrapped_1_div_scaled")]; + tensor wrapped_1 = sub(x = abs_idx_1, y = wrapped_1_div_scaled)[name = tensor("wrapped_1")]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 16, 1, 1])]; + tensor var_202 = reshape(shape = var_201, x = wrapped_1)[name = tensor("op_202")]; + tensor write_indexes_1_reps_0 = const()[name = tensor("write_indexes_1_reps_0"), val = tensor([1, 1, 8, 64])]; + tensor write_indexes_1 = tile(reps = write_indexes_1_reps_0, x = var_202)[name = tensor("write_indexes_1")]; + tensor var_205_begin_0 = const()[name = tensor("op_205_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_205_end_0 = const()[name = tensor("op_205_end_0"), val = tensor([1, 1, 256, 8, 64])]; + tensor var_205_end_mask_0 = const()[name = tensor("op_205_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_205_squeeze_mask_0 = const()[name = tensor("op_205_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_205 = slice_by_index(begin = var_205_begin_0, end = var_205_end_0, end_mask = var_205_end_mask_0, squeeze_mask = var_205_squeeze_mask_0, x = attn0_cache)[name = tensor("op_205")]; + tensor new_k_cache_1_axis_0 = const()[name = tensor("new_k_cache_1_axis_0"), val = tensor(1)]; + tensor new_k_cache_1_mode_0 = const()[name = tensor("new_k_cache_1_mode_0"), val = tensor("update")]; + tensor new_k_cache_1_validate_indices_0 = const()[name = tensor("new_k_cache_1_validate_indices_0"), val = tensor(false)]; + tensor new_k_cache_1 = scatter_along_axis(axis = new_k_cache_1_axis_0, data = var_205, indices = write_indexes_1, mode = new_k_cache_1_mode_0, updates = k_5, validate_indices = new_k_cache_1_validate_indices_0)[name = tensor("new_k_cache_1")]; + tensor var_207_begin_0 = const()[name = tensor("op_207_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_207_end_0 = const()[name = tensor("op_207_end_0"), val = tensor([2, 1, 256, 8, 64])]; + tensor var_207_end_mask_0 = const()[name = tensor("op_207_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_207_squeeze_mask_0 = const()[name = tensor("op_207_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_207 = slice_by_index(begin = var_207_begin_0, end = var_207_end_0, end_mask = var_207_end_mask_0, squeeze_mask = var_207_squeeze_mask_0, x = attn0_cache)[name = tensor("op_207")]; + tensor new_v_cache_1_axis_0 = const()[name = tensor("new_v_cache_1_axis_0"), val = tensor(1)]; + tensor new_v_cache_1_mode_0 = const()[name = tensor("new_v_cache_1_mode_0"), val = tensor("update")]; + tensor new_v_cache_1_validate_indices_0 = const()[name = tensor("new_v_cache_1_validate_indices_0"), val = tensor(false)]; + tensor new_v_cache_1 = scatter_along_axis(axis = new_v_cache_1_axis_0, data = var_207, indices = write_indexes_1, mode = new_v_cache_1_mode_0, updates = squeeze_2, validate_indices = new_v_cache_1_validate_indices_0)[name = tensor("new_v_cache_1")]; + tensor var_210_axis_0 = const()[name = tensor("op_210_axis_0"), val = tensor(0)]; + tensor var_210 = stack(axis = var_210_axis_0, values = (new_k_cache_1, new_v_cache_1))[name = tensor("op_210")]; + tensor var_211 = not_equal(x = new_k_cache_1, y = new_k_cache_1)[name = tensor("op_211")]; + tensor var_212 = const()[name = tensor("op_212"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230976)))]; + tensor new_k_cache_3 = select(a = var_212, b = new_k_cache_1, cond = var_211)[name = tensor("new_k_cache_3")]; + tensor var_214 = not_equal(x = new_v_cache_1, y = new_v_cache_1)[name = tensor("op_214")]; + tensor new_v_cache_3 = select(a = var_212, b = new_v_cache_1, cond = var_214)[name = tensor("new_v_cache_3")]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([0, 2, 1, 3])]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; + tensor var_222 = reshape(shape = var_221, x = attn0_offset)[name = tensor("op_222")]; + tensor var_224_promoted = const()[name = tensor("op_224_promoted"), val = tensor([0x1.ep+3])]; + tensor var_225 = add(x = var_222, y = var_224_promoted)[name = tensor("op_225")]; + tensor last_pos_1_dtype_0 = const()[name = tensor("last_pos_1_dtype_0"), val = tensor("int32")]; + tensor slot_idx_1 = const()[name = tensor("slot_idx_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255]])]; + tensor last_pos_1 = cast(dtype = last_pos_1_dtype_0, x = var_225)[name = tensor("cast_48")]; + tensor diff_1 = sub(x = last_pos_1, y = slot_idx_1)[name = tensor("diff_1")]; + tensor var_231_div = floor_div(x = diff_1, y = capacity_1)[name = tensor("op_231_div")]; + tensor var_231_div_scaled = mul(x = var_231_div, y = capacity_1)[name = tensor("op_231_div_scaled")]; + tensor var_231 = sub(x = diff_1, y = var_231_div_scaled)[name = tensor("op_231")]; + tensor pos_k_1 = sub(x = last_pos_1, y = var_231)[name = tensor("pos_k_1")]; + tensor var_237_promoted = const()[name = tensor("op_237_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41755328)))]; + tensor pos_q_1 = add(x = var_222, y = var_237_promoted)[name = tensor("pos_q_1")]; + tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([2])]; + tensor var_241 = expand_dims(axes = var_241_axes_0, x = pos_q_1)[name = tensor("op_241")]; + tensor var_243_axes_0 = const()[name = tensor("op_243_axes_0"), val = tensor([1])]; + tensor var_243 = expand_dims(axes = var_243_axes_0, x = pos_k_1)[name = tensor("op_243")]; + tensor var_244_promoted_dtype_0 = const()[name = tensor("op_244_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_244_promoted = cast(dtype = var_244_promoted_dtype_0, x = var_243)[name = tensor("cast_47")]; + tensor delta_1 = sub(x = var_241, y = var_244_promoted)[name = tensor("delta_1")]; + tensor valid_1 = greater_equal(x = var_243, y = var_86)[name = tensor("valid_1")]; + tensor var_253 = const()[name = tensor("op_253"), val = tensor([1, 1, 1])]; + tensor var_254 = reshape(shape = var_253, x = attn0_offset)[name = tensor("op_254")]; + tensor var_256_promoted = const()[name = tensor("op_256_promoted"), val = tensor([0x1.ep+3])]; + tensor var_257 = add(x = var_254, y = var_256_promoted)[name = tensor("op_257")]; + tensor var_258 = less_equal(x = var_244_promoted, y = var_257)[name = tensor("op_258")]; + tensor valid_3 = logical_and(x = valid_1, y = var_258)[name = tensor("valid_3")]; + tensor var_86_promoted = const()[name = tensor("op_86_promoted"), val = tensor(0x0p+0)]; + tensor var_260 = greater_equal(x = delta_1, y = var_86_promoted)[name = tensor("op_260")]; + tensor attn_mask_1 = logical_and(x = valid_3, y = var_260)[name = tensor("attn_mask_1")]; + tensor var_98_promoted = const()[name = tensor("op_98_promoted"), val = tensor(0x1.f4p+7)]; + tensor var_262 = less(x = delta_1, y = var_98_promoted)[name = tensor("op_262")]; + tensor attn_mask_3 = logical_and(x = attn_mask_1, y = var_262)[name = tensor("attn_mask_3")]; + tensor attn_mask_5_axes_0 = const()[name = tensor("attn_mask_5_axes_0"), val = tensor([1])]; + tensor attn_mask_5 = expand_dims(axes = attn_mask_5_axes_0, x = attn_mask_3)[name = tensor("attn_mask_5")]; + tensor var_267_transpose_x_0 = const()[name = tensor("op_267_transpose_x_0"), val = tensor(false)]; + tensor var_267_transpose_y_0 = const()[name = tensor("op_267_transpose_y_0"), val = tensor(false)]; + tensor transpose_6_perm_0 = const()[name = tensor("transpose_6_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_7_perm_0 = const()[name = tensor("transpose_7_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_7 = transpose(perm = transpose_7_perm_0, x = new_k_cache_3)[name = tensor("transpose_16")]; + tensor transpose_6 = transpose(perm = transpose_6_perm_0, x = q_5)[name = tensor("transpose_17")]; + tensor var_267 = matmul(transpose_x = var_267_transpose_x_0, transpose_y = var_267_transpose_y_0, x = transpose_6, y = transpose_7)[name = tensor("op_267")]; + tensor var_268 = const()[name = tensor("op_268"), val = tensor(0x1p-3)]; + tensor attn_1 = mul(x = var_267, y = var_268)[name = tensor("attn_1")]; + tensor var_270 = logical_not(x = attn_mask_5)[name = tensor("op_270")]; + tensor attn_3 = select(a = var_100, b = attn_1, cond = var_270)[name = tensor("attn_3")]; + tensor attn_5 = softmax(axis = var_91, x = attn_3)[name = tensor("attn_5")]; + tensor x_5_transpose_x_0 = const()[name = tensor("x_5_transpose_x_0"), val = tensor(false)]; + tensor x_5_transpose_y_0 = const()[name = tensor("x_5_transpose_y_0"), val = tensor(false)]; + tensor v_attn_1 = transpose(perm = var_219, x = new_v_cache_3)[name = tensor("transpose_18")]; + tensor x_5 = matmul(transpose_x = x_5_transpose_x_0, transpose_y = x_5_transpose_y_0, x = attn_5, y = v_attn_1)[name = tensor("x_5")]; + tensor var_274_perm_0 = const()[name = tensor("op_274_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_275 = const()[name = tensor("op_275"), val = tensor([1, 16, 512])]; + tensor var_274 = transpose(perm = var_274_perm_0, x = x_5)[name = tensor("transpose_15")]; + tensor input_5 = reshape(shape = var_275, x = var_274)[name = tensor("input_5")]; + tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41755456)))]; + tensor x_7 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight, x = input_5)[name = tensor("linear_1")]; + tensor var_284 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale, y = x_7)[name = tensor("op_284")]; + tensor input_7 = add(x = input_3, y = var_284)[name = tensor("input_7")]; + tensor input_9_axes_0 = const()[name = tensor("input_9_axes_0"), val = tensor([-1])]; + tensor input_9 = layer_norm(axes = input_9_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm2_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_0_norm2_weight, x = input_7)[name = tensor("input_9")]; + tensor linear_2_bias_0 = const()[name = tensor("linear_2_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41757568)))]; + tensor var_291 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear1_weight, x = input_9)[name = tensor("linear_2")]; + tensor input_11_mode_0 = const()[name = tensor("input_11_mode_0"), val = tensor("EXACT")]; + tensor input_11 = gelu(mode = input_11_mode_0, x = var_291)[name = tensor("input_11")]; + tensor x_9 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear2_weight, x = input_11)[name = tensor("linear_3")]; + tensor var_297 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale, y = x_9)[name = tensor("op_297")]; + tensor input_13 = add(x = input_7, y = var_297)[name = tensor("input_13")]; + tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; + tensor query = layer_norm(axes = query_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm1_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_1_norm1_weight, x = input_13)[name = tensor("query")]; + tensor projected = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight, x = query)[name = tensor("linear_4")]; + tensor var_320 = const()[name = tensor("op_320"), val = tensor([1, 16, 3, 8, 64])]; + tensor packed = reshape(shape = var_320, x = projected)[name = tensor("packed")]; + tensor var_322_split_sizes_0 = const()[name = tensor("op_322_split_sizes_0"), val = tensor([1, 1, 1])]; + tensor var_322_axis_0 = const()[name = tensor("op_322_axis_0"), val = tensor(2)]; + tensor var_322_0, tensor var_322_1, tensor var_322_2 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = packed)[name = tensor("op_322")]; + tensor squeeze_3_axes_0 = const()[name = tensor("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = var_322_0)[name = tensor("squeeze_3")]; + tensor squeeze_4_axes_0 = const()[name = tensor("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4 = squeeze(axes = squeeze_4_axes_0, x = var_322_1)[name = tensor("squeeze_4")]; + tensor squeeze_5_axes_0 = const()[name = tensor("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5 = squeeze(axes = squeeze_5_axes_0, x = var_322_2)[name = tensor("squeeze_5")]; + tensor offset_begin_0 = const()[name = tensor("offset_begin_0"), val = tensor([0])]; + tensor offset_end_0 = const()[name = tensor("offset_end_0"), val = tensor([1])]; + tensor offset_end_mask_0 = const()[name = tensor("offset_end_mask_0"), val = tensor([false])]; + tensor offset_squeeze_mask_0 = const()[name = tensor("offset_squeeze_mask_0"), val = tensor([true])]; + tensor offset = slice_by_index(begin = offset_begin_0, end = offset_end_0, end_mask = offset_end_mask_0, squeeze_mask = offset_squeeze_mask_0, x = attn1_offset)[name = tensor("offset")]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41765824)))]; + tensor ts_7_promoted = const()[name = tensor("ts_7_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766016)))]; + tensor ts_9 = add(x = ts_7_promoted, y = offset)[name = tensor("ts_9")]; + tensor var_338 = const()[name = tensor("op_338"), val = tensor([-1, 1, 1])]; + tensor ts = reshape(shape = var_338, x = ts_9)[name = tensor("ts")]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 16, 8, 32, 2])]; + tensor q_9 = reshape(shape = var_340, x = squeeze_3)[name = tensor("q_9")]; + tensor var_342 = const()[name = tensor("op_342"), val = tensor([1, 16, 8, 32, 2])]; + tensor k_9 = reshape(shape = var_342, x = squeeze_4)[name = tensor("k_9")]; + tensor var_344_begin_0 = const()[name = tensor("op_344_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_344_end_0 = const()[name = tensor("op_344_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_344_end_mask_0 = const()[name = tensor("op_344_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_344_squeeze_mask_0 = const()[name = tensor("op_344_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_344 = slice_by_index(begin = var_344_begin_0, end = var_344_end_0, end_mask = var_344_end_mask_0, squeeze_mask = var_344_squeeze_mask_0, x = q_9)[name = tensor("op_344")]; + tensor var_346_begin_0 = const()[name = tensor("op_346_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_346_end_0 = const()[name = tensor("op_346_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_346_end_mask_0 = const()[name = tensor("op_346_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_346_squeeze_mask_0 = const()[name = tensor("op_346_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_346 = slice_by_index(begin = var_346_begin_0, end = var_346_end_0, end_mask = var_346_end_mask_0, squeeze_mask = var_346_squeeze_mask_0, x = q_9)[name = tensor("op_346")]; + tensor var_348_begin_0 = const()[name = tensor("op_348_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_348_end_0 = const()[name = tensor("op_348_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_348_end_mask_0 = const()[name = tensor("op_348_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_348_squeeze_mask_0 = const()[name = tensor("op_348_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_348 = slice_by_index(begin = var_348_begin_0, end = var_348_end_0, end_mask = var_348_end_mask_0, squeeze_mask = var_348_squeeze_mask_0, x = k_9)[name = tensor("op_348")]; + tensor var_350_begin_0 = const()[name = tensor("op_350_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_350_end_0 = const()[name = tensor("op_350_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_350_end_mask_0 = const()[name = tensor("op_350_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_350_squeeze_mask_0 = const()[name = tensor("op_350_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_350 = slice_by_index(begin = var_350_begin_0, end = var_350_end_0, end_mask = var_350_end_mask_0, squeeze_mask = var_350_squeeze_mask_0, x = k_9)[name = tensor("op_350")]; + tensor var_352 = mul(x = freqs, y = ts)[name = tensor("op_352")]; + tensor rotr = cos(x = var_352)[name = tensor("rotr")]; + tensor roti = sin(x = var_352)[name = tensor("roti")]; + tensor var_356 = mul(x = var_344, y = rotr)[name = tensor("op_356")]; + tensor var_357 = mul(x = var_346, y = roti)[name = tensor("op_357")]; + tensor qor_5 = sub(x = var_356, y = var_357)[name = tensor("qor_5")]; + tensor var_359 = mul(x = var_344, y = roti)[name = tensor("op_359")]; + tensor var_360 = mul(x = var_346, y = rotr)[name = tensor("op_360")]; + tensor qoi_5 = add(x = var_359, y = var_360)[name = tensor("qoi_5")]; + tensor var_362 = mul(x = var_348, y = rotr)[name = tensor("op_362")]; + tensor var_363 = mul(x = var_350, y = roti)[name = tensor("op_363")]; + tensor kor_5 = sub(x = var_362, y = var_363)[name = tensor("kor_5")]; + tensor var_365 = mul(x = var_348, y = roti)[name = tensor("op_365")]; + tensor var_366 = mul(x = var_350, y = rotr)[name = tensor("op_366")]; + tensor koi_5 = add(x = var_365, y = var_366)[name = tensor("koi_5")]; + tensor qo_axis_0 = const()[name = tensor("qo_axis_0"), val = tensor(-1)]; + tensor qo = stack(axis = qo_axis_0, values = (qor_5, qoi_5))[name = tensor("qo")]; + tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; + tensor ko = stack(axis = ko_axis_0, values = (kor_5, koi_5))[name = tensor("ko")]; + tensor var_376 = const()[name = tensor("op_376"), val = tensor([1, 16, 8, 64])]; + tensor q = reshape(shape = var_376, x = qo)[name = tensor("q")]; + tensor var_378 = const()[name = tensor("op_378"), val = tensor([1, 16, 8, 64])]; + tensor k = reshape(shape = var_378, x = ko)[name = tensor("k")]; + tensor capacity = const()[name = tensor("capacity"), val = tensor([256])]; + tensor var_383_dtype_0 = const()[name = tensor("op_383_dtype_0"), val = tensor("int32")]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; + tensor var_383 = cast(dtype = var_383_dtype_0, x = attn1_offset)[name = tensor("cast_46")]; + tensor write_base = reshape(shape = var_384, x = var_383)[name = tensor("write_base")]; + tensor write_range = const()[name = tensor("write_range"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; + tensor abs_idx = add(x = write_base, y = write_range)[name = tensor("abs_idx")]; + tensor wrapped_div = floor_div(x = abs_idx, y = capacity)[name = tensor("wrapped_div")]; + tensor wrapped_div_scaled = mul(x = wrapped_div, y = capacity)[name = tensor("wrapped_div_scaled")]; + tensor wrapped = sub(x = abs_idx, y = wrapped_div_scaled)[name = tensor("wrapped")]; + tensor var_391 = const()[name = tensor("op_391"), val = tensor([1, 16, 1, 1])]; + tensor var_392 = reshape(shape = var_391, x = wrapped)[name = tensor("op_392")]; + tensor write_indexes_reps_0 = const()[name = tensor("write_indexes_reps_0"), val = tensor([1, 1, 8, 64])]; + tensor write_indexes = tile(reps = write_indexes_reps_0, x = var_392)[name = tensor("write_indexes")]; + tensor var_395_begin_0 = const()[name = tensor("op_395_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_395_end_0 = const()[name = tensor("op_395_end_0"), val = tensor([1, 1, 256, 8, 64])]; + tensor var_395_end_mask_0 = const()[name = tensor("op_395_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_395_squeeze_mask_0 = const()[name = tensor("op_395_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_395 = slice_by_index(begin = var_395_begin_0, end = var_395_end_0, end_mask = var_395_end_mask_0, squeeze_mask = var_395_squeeze_mask_0, x = attn1_cache)[name = tensor("op_395")]; + tensor new_k_cache_5_axis_0 = const()[name = tensor("new_k_cache_5_axis_0"), val = tensor(1)]; + tensor new_k_cache_5_mode_0 = const()[name = tensor("new_k_cache_5_mode_0"), val = tensor("update")]; + tensor new_k_cache_5_validate_indices_0 = const()[name = tensor("new_k_cache_5_validate_indices_0"), val = tensor(false)]; + tensor new_k_cache_5 = scatter_along_axis(axis = new_k_cache_5_axis_0, data = var_395, indices = write_indexes, mode = new_k_cache_5_mode_0, updates = k, validate_indices = new_k_cache_5_validate_indices_0)[name = tensor("new_k_cache_5")]; + tensor var_397_begin_0 = const()[name = tensor("op_397_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_397_end_0 = const()[name = tensor("op_397_end_0"), val = tensor([2, 1, 256, 8, 64])]; + tensor var_397_end_mask_0 = const()[name = tensor("op_397_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_397_squeeze_mask_0 = const()[name = tensor("op_397_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_397 = slice_by_index(begin = var_397_begin_0, end = var_397_end_0, end_mask = var_397_end_mask_0, squeeze_mask = var_397_squeeze_mask_0, x = attn1_cache)[name = tensor("op_397")]; + tensor new_v_cache_5_axis_0 = const()[name = tensor("new_v_cache_5_axis_0"), val = tensor(1)]; + tensor new_v_cache_5_mode_0 = const()[name = tensor("new_v_cache_5_mode_0"), val = tensor("update")]; + tensor new_v_cache_5_validate_indices_0 = const()[name = tensor("new_v_cache_5_validate_indices_0"), val = tensor(false)]; + tensor new_v_cache_5 = scatter_along_axis(axis = new_v_cache_5_axis_0, data = var_397, indices = write_indexes, mode = new_v_cache_5_mode_0, updates = squeeze_5, validate_indices = new_v_cache_5_validate_indices_0)[name = tensor("new_v_cache_5")]; + tensor var_400_axis_0 = const()[name = tensor("op_400_axis_0"), val = tensor(0)]; + tensor var_400 = stack(axis = var_400_axis_0, values = (new_k_cache_5, new_v_cache_5))[name = tensor("op_400")]; + tensor var_401 = not_equal(x = new_k_cache_5, y = new_k_cache_5)[name = tensor("op_401")]; + tensor new_k_cache = select(a = var_212, b = new_k_cache_5, cond = var_401)[name = tensor("new_k_cache")]; + tensor var_404 = not_equal(x = new_v_cache_5, y = new_v_cache_5)[name = tensor("op_404")]; + tensor new_v_cache = select(a = var_212, b = new_v_cache_5, cond = var_404)[name = tensor("new_v_cache")]; + tensor var_409 = const()[name = tensor("op_409"), val = tensor([0, 2, 1, 3])]; + tensor var_411 = const()[name = tensor("op_411"), val = tensor([1, 1])]; + tensor var_412 = reshape(shape = var_411, x = attn1_offset)[name = tensor("op_412")]; + tensor var_414_promoted = const()[name = tensor("op_414_promoted"), val = tensor([0x1.ep+3])]; + tensor var_415 = add(x = var_412, y = var_414_promoted)[name = tensor("op_415")]; + tensor last_pos_dtype_0 = const()[name = tensor("last_pos_dtype_0"), val = tensor("int32")]; + tensor last_pos = cast(dtype = last_pos_dtype_0, x = var_415)[name = tensor("cast_45")]; + tensor diff = sub(x = last_pos, y = slot_idx_1)[name = tensor("diff")]; + tensor var_421_div = floor_div(x = diff, y = capacity)[name = tensor("op_421_div")]; + tensor var_421_div_scaled = mul(x = var_421_div, y = capacity)[name = tensor("op_421_div_scaled")]; + tensor var_421 = sub(x = diff, y = var_421_div_scaled)[name = tensor("op_421")]; + tensor pos_k = sub(x = last_pos, y = var_421)[name = tensor("pos_k")]; + tensor var_427_promoted = const()[name = tensor("op_427_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766144)))]; + tensor pos_q = add(x = var_412, y = var_427_promoted)[name = tensor("pos_q")]; + tensor var_431_axes_0 = const()[name = tensor("op_431_axes_0"), val = tensor([2])]; + tensor var_431 = expand_dims(axes = var_431_axes_0, x = pos_q)[name = tensor("op_431")]; + tensor var_433_axes_0 = const()[name = tensor("op_433_axes_0"), val = tensor([1])]; + tensor var_433 = expand_dims(axes = var_433_axes_0, x = pos_k)[name = tensor("op_433")]; + tensor var_434_promoted_dtype_0 = const()[name = tensor("op_434_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_434_promoted = cast(dtype = var_434_promoted_dtype_0, x = var_433)[name = tensor("cast_44")]; + tensor delta = sub(x = var_431, y = var_434_promoted)[name = tensor("delta")]; + tensor valid_5 = greater_equal(x = var_433, y = var_86)[name = tensor("valid_5")]; + tensor var_443 = const()[name = tensor("op_443"), val = tensor([1, 1, 1])]; + tensor var_444 = reshape(shape = var_443, x = attn1_offset)[name = tensor("op_444")]; + tensor var_446_promoted = const()[name = tensor("op_446_promoted"), val = tensor([0x1.ep+3])]; + tensor var_447 = add(x = var_444, y = var_446_promoted)[name = tensor("op_447")]; + tensor var_448 = less_equal(x = var_434_promoted, y = var_447)[name = tensor("op_448")]; + tensor valid = logical_and(x = valid_5, y = var_448)[name = tensor("valid")]; + tensor var_86_promoted_1 = const()[name = tensor("op_86_promoted_1"), val = tensor(0x0p+0)]; + tensor var_450 = greater_equal(x = delta, y = var_86_promoted_1)[name = tensor("op_450")]; + tensor attn_mask_7 = logical_and(x = valid, y = var_450)[name = tensor("attn_mask_7")]; + tensor var_98_promoted_1 = const()[name = tensor("op_98_promoted_1"), val = tensor(0x1.f4p+7)]; + tensor var_452 = less(x = delta, y = var_98_promoted_1)[name = tensor("op_452")]; + tensor attn_mask_9 = logical_and(x = attn_mask_7, y = var_452)[name = tensor("attn_mask_9")]; + tensor attn_mask_axes_0 = const()[name = tensor("attn_mask_axes_0"), val = tensor([1])]; + tensor attn_mask = expand_dims(axes = attn_mask_axes_0, x = attn_mask_9)[name = tensor("attn_mask")]; + tensor var_457_transpose_x_0 = const()[name = tensor("op_457_transpose_x_0"), val = tensor(false)]; + tensor var_457_transpose_y_0 = const()[name = tensor("op_457_transpose_y_0"), val = tensor(false)]; + tensor transpose_8_perm_0 = const()[name = tensor("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_9_perm_0 = const()[name = tensor("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = new_k_cache)[name = tensor("transpose_12")]; + tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = q)[name = tensor("transpose_13")]; + tensor var_457 = matmul(transpose_x = var_457_transpose_x_0, transpose_y = var_457_transpose_y_0, x = transpose_8, y = transpose_9)[name = tensor("op_457")]; + tensor var_458 = const()[name = tensor("op_458"), val = tensor(0x1p-3)]; + tensor attn_7 = mul(x = var_457, y = var_458)[name = tensor("attn_7")]; + tensor var_460 = logical_not(x = attn_mask)[name = tensor("op_460")]; + tensor attn_9 = select(a = var_100, b = attn_7, cond = var_460)[name = tensor("attn_9")]; + tensor attn = softmax(axis = var_91, x = attn_9)[name = tensor("attn")]; + tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; + tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; + tensor v_attn = transpose(perm = var_409, x = new_v_cache)[name = tensor("transpose_14")]; + tensor x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = attn, y = v_attn)[name = tensor("x_11")]; + tensor var_464_perm_0 = const()[name = tensor("op_464_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 16, 512])]; + tensor var_464 = transpose(perm = var_464_perm_0, x = x_11)[name = tensor("transpose_11")]; + tensor input_15 = reshape(shape = var_465, x = var_464)[name = tensor("input_15")]; + tensor x_13 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight, x = input_15)[name = tensor("linear_5")]; + tensor var_474 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale, y = x_13)[name = tensor("op_474")]; + tensor input_17 = add(x = input_13, y = var_474)[name = tensor("input_17")]; + tensor input_19_axes_0 = const()[name = tensor("input_19_axes_0"), val = tensor([-1])]; + tensor input_19 = layer_norm(axes = input_19_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm2_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_1_norm2_weight, x = input_17)[name = tensor("input_19")]; + tensor var_481 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear1_weight, x = input_19)[name = tensor("linear_6")]; + tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("EXACT")]; + tensor input_21 = gelu(mode = input_21_mode_0, x = var_481)[name = tensor("input_21")]; + tensor x_15 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear2_weight, x = input_21)[name = tensor("linear_7")]; + tensor var_487 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale, y = x_15)[name = tensor("op_487")]; + tensor z = add(x = input_17, y = var_487)[name = tensor("z")]; + tensor x_17_perm_0 = const()[name = tensor("x_17_perm_0"), val = tensor([0, 2, 1])]; + tensor var_507 = const()[name = tensor("op_507"), val = tensor(0x1p+0)]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor(-1)]; + tensor input_23_interleave_0 = const()[name = tensor("input_23_interleave_0"), val = tensor(false)]; + tensor x_17 = transpose(perm = x_17_perm_0, x = z)[name = tensor("transpose_10")]; + tensor input_23 = concat(axis = var_508, interleave = input_23_interleave_0, values = (conv0_prev, x_17))[name = tensor("input_23")]; + tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("valid")]; + tensor input_25_strides_0 = const()[name = tensor("input_25_strides_0"), val = tensor([1])]; + tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([0, 0])]; + tensor input_25_dilations_0 = const()[name = tensor("input_25_dilations_0"), val = tensor([1])]; + tensor input_25_groups_0 = const()[name = tensor("input_25_groups_0"), val = tensor(1)]; + tensor input_25 = conv(bias = mimi_decoder_model_0_conv_bias, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = mimi_decoder_model_0_conv_weight, x = input_23)[name = tensor("input_25")]; + tensor var_542_begin_0 = const()[name = tensor("op_542_begin_0"), val = tensor([0, 0, 16])]; + tensor var_542_end_0 = const()[name = tensor("op_542_end_0"), val = tensor([1, 512, 22])]; + tensor var_542_end_mask_0 = const()[name = tensor("op_542_end_mask_0"), val = tensor([true, true, true])]; + tensor var_542 = slice_by_index(begin = var_542_begin_0, end = var_542_end_0, end_mask = var_542_end_mask_0, x = input_23)[name = tensor("op_542")]; + tensor input_27 = elu(alpha = var_507, x = input_25)[name = tensor("input_27")]; + tensor y_5_pad_type_0 = const()[name = tensor("y_5_pad_type_0"), val = tensor("valid")]; + tensor y_5_strides_0 = const()[name = tensor("y_5_strides_0"), val = tensor([6])]; + tensor y_5_pad_0 = const()[name = tensor("y_5_pad_0"), val = tensor([0, 0])]; + tensor y_5_dilations_0 = const()[name = tensor("y_5_dilations_0"), val = tensor([1])]; + tensor y_5_groups_0 = const()[name = tensor("y_5_groups_0"), val = tensor(1)]; + tensor y_5_has_output_shape_output_shape_0 = const()[name = tensor("y_5_has_output_shape_output_shape_0"), val = tensor([1, 256, 102])]; + tensor y_5_has_output_shape = conv_transpose(bias = mimi_decoder_model_2_convtr_bias, dilations = y_5_dilations_0, groups = y_5_groups_0, output_shape = y_5_has_output_shape_output_shape_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = mimi_decoder_model_2_convtr_weight, x = input_27)[name = tensor("y_5_has_output_shape")]; + tensor var_557_begin_0 = const()[name = tensor("op_557_begin_0"), val = tensor([0, 0, 0])]; + tensor var_557_end_0 = const()[name = tensor("op_557_end_0"), val = tensor([1, 256, 6])]; + tensor var_557_end_mask_0 = const()[name = tensor("op_557_end_mask_0"), val = tensor([true, true, false])]; + tensor var_557 = slice_by_index(begin = var_557_begin_0, end = var_557_end_0, end_mask = var_557_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_557")]; + tensor var_558 = add(x = var_557, y = convtr0_partial)[name = tensor("op_558")]; + tensor var_559_begin_0 = const()[name = tensor("op_559_begin_0"), val = tensor([0, 0, 6])]; + tensor var_559_end_0 = const()[name = tensor("op_559_end_0"), val = tensor([1, 256, 102])]; + tensor var_559_end_mask_0 = const()[name = tensor("op_559_end_mask_0"), val = tensor([true, true, true])]; + tensor var_559 = slice_by_index(begin = var_559_begin_0, end = var_559_end_0, end_mask = var_559_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_559")]; + tensor y_7_interleave_0 = const()[name = tensor("y_7_interleave_0"), val = tensor(false)]; + tensor y_7 = concat(axis = var_508, interleave = y_7_interleave_0, values = (var_558, var_559))[name = tensor("y_7")]; + tensor new_partial_1_begin_0 = const()[name = tensor("new_partial_1_begin_0"), val = tensor([0, 0, 96])]; + tensor new_partial_1_end_0 = const()[name = tensor("new_partial_1_end_0"), val = tensor([1, 256, 102])]; + tensor new_partial_1_end_mask_0 = const()[name = tensor("new_partial_1_end_mask_0"), val = tensor([true, true, true])]; + tensor new_partial_1 = slice_by_index(begin = new_partial_1_begin_0, end = new_partial_1_end_0, end_mask = new_partial_1_end_mask_0, x = y_7)[name = tensor("new_partial_1")]; + tensor var_564 = const()[name = tensor("op_564"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766272)))]; + tensor var_565 = sub(x = new_partial_1, y = var_564)[name = tensor("op_565")]; + tensor input_29_begin_0 = const()[name = tensor("input_29_begin_0"), val = tensor([0, 0, 0])]; + tensor input_29_end_0 = const()[name = tensor("input_29_end_0"), val = tensor([1, 256, 96])]; + tensor input_29_end_mask_0 = const()[name = tensor("input_29_end_mask_0"), val = tensor([true, true, false])]; + tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, x = y_7)[name = tensor("input_29")]; + tensor x_19 = elu(alpha = var_507, x = input_29)[name = tensor("x_19")]; + tensor input_31_interleave_0 = const()[name = tensor("input_31_interleave_0"), val = tensor(false)]; + tensor input_31 = concat(axis = var_508, interleave = input_31_interleave_0, values = (res0_conv0_prev, x_19))[name = tensor("input_31")]; + tensor input_33_pad_type_0 = const()[name = tensor("input_33_pad_type_0"), val = tensor("valid")]; + tensor input_33_strides_0 = const()[name = tensor("input_33_strides_0"), val = tensor([1])]; + tensor input_33_pad_0 = const()[name = tensor("input_33_pad_0"), val = tensor([0, 0])]; + tensor input_33_dilations_0 = const()[name = tensor("input_33_dilations_0"), val = tensor([1])]; + tensor input_33_groups_0 = const()[name = tensor("input_33_groups_0"), val = tensor(1)]; + tensor input_33 = conv(bias = mimi_decoder_model_3_block_1_conv_bias, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = mimi_decoder_model_3_block_1_conv_weight, x = input_31)[name = tensor("input_33")]; + tensor var_585_begin_0 = const()[name = tensor("op_585_begin_0"), val = tensor([0, 0, 96])]; + tensor var_585_end_0 = const()[name = tensor("op_585_end_0"), val = tensor([1, 256, 98])]; + tensor var_585_end_mask_0 = const()[name = tensor("op_585_end_mask_0"), val = tensor([true, true, true])]; + tensor var_585 = slice_by_index(begin = var_585_begin_0, end = var_585_end_0, end_mask = var_585_end_mask_0, x = input_31)[name = tensor("op_585")]; + tensor x_21 = elu(alpha = var_507, x = input_33)[name = tensor("x_21")]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("valid")]; + tensor v_5_strides_0 = const()[name = tensor("v_5_strides_0"), val = tensor([1])]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0])]; + tensor v_5_dilations_0 = const()[name = tensor("v_5_dilations_0"), val = tensor([1])]; + tensor v_5_groups_0 = const()[name = tensor("v_5_groups_0"), val = tensor(1)]; + tensor v_5 = conv(bias = mimi_decoder_model_3_block_3_conv_bias, dilations = v_5_dilations_0, groups = v_5_groups_0, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = v_5_strides_0, weight = mimi_decoder_model_3_block_3_conv_weight, x = x_21)[name = tensor("v_5")]; + tensor input_35 = add(x = input_29, y = v_5)[name = tensor("input_35")]; + tensor input_37 = elu(alpha = var_507, x = input_35)[name = tensor("input_37")]; + tensor y_9_pad_type_0 = const()[name = tensor("y_9_pad_type_0"), val = tensor("valid")]; + tensor y_9_strides_0 = const()[name = tensor("y_9_strides_0"), val = tensor([5])]; + tensor y_9_pad_0 = const()[name = tensor("y_9_pad_0"), val = tensor([0, 0])]; + tensor y_9_dilations_0 = const()[name = tensor("y_9_dilations_0"), val = tensor([1])]; + tensor y_9_groups_0 = const()[name = tensor("y_9_groups_0"), val = tensor(1)]; + tensor y_9_has_output_shape_output_shape_0 = const()[name = tensor("y_9_has_output_shape_output_shape_0"), val = tensor([1, 128, 485])]; + tensor y_9_has_output_shape = conv_transpose(bias = mimi_decoder_model_5_convtr_bias, dilations = y_9_dilations_0, groups = y_9_groups_0, output_shape = y_9_has_output_shape_output_shape_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = mimi_decoder_model_5_convtr_weight, x = input_37)[name = tensor("y_9_has_output_shape")]; + tensor var_613_begin_0 = const()[name = tensor("op_613_begin_0"), val = tensor([0, 0, 0])]; + tensor var_613_end_0 = const()[name = tensor("op_613_end_0"), val = tensor([1, 128, 5])]; + tensor var_613_end_mask_0 = const()[name = tensor("op_613_end_mask_0"), val = tensor([true, true, false])]; + tensor var_613 = slice_by_index(begin = var_613_begin_0, end = var_613_end_0, end_mask = var_613_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_613")]; + tensor var_614 = add(x = var_613, y = convtr1_partial)[name = tensor("op_614")]; + tensor var_615_begin_0 = const()[name = tensor("op_615_begin_0"), val = tensor([0, 0, 5])]; + tensor var_615_end_0 = const()[name = tensor("op_615_end_0"), val = tensor([1, 128, 485])]; + tensor var_615_end_mask_0 = const()[name = tensor("op_615_end_mask_0"), val = tensor([true, true, true])]; + tensor var_615 = slice_by_index(begin = var_615_begin_0, end = var_615_end_0, end_mask = var_615_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_615")]; + tensor y_11_interleave_0 = const()[name = tensor("y_11_interleave_0"), val = tensor(false)]; + tensor y_11 = concat(axis = var_508, interleave = y_11_interleave_0, values = (var_614, var_615))[name = tensor("y_11")]; + tensor new_partial_3_begin_0 = const()[name = tensor("new_partial_3_begin_0"), val = tensor([0, 0, 480])]; + tensor new_partial_3_end_0 = const()[name = tensor("new_partial_3_end_0"), val = tensor([1, 128, 485])]; + tensor new_partial_3_end_mask_0 = const()[name = tensor("new_partial_3_end_mask_0"), val = tensor([true, true, true])]; + tensor new_partial_3 = slice_by_index(begin = new_partial_3_begin_0, end = new_partial_3_end_0, end_mask = new_partial_3_end_mask_0, x = y_11)[name = tensor("new_partial_3")]; + tensor var_620 = const()[name = tensor("op_620"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41767360)))]; + tensor var_621 = sub(x = new_partial_3, y = var_620)[name = tensor("op_621")]; + tensor input_39_begin_0 = const()[name = tensor("input_39_begin_0"), val = tensor([0, 0, 0])]; + tensor input_39_end_0 = const()[name = tensor("input_39_end_0"), val = tensor([1, 128, 480])]; + tensor input_39_end_mask_0 = const()[name = tensor("input_39_end_mask_0"), val = tensor([true, true, false])]; + tensor input_39 = slice_by_index(begin = input_39_begin_0, end = input_39_end_0, end_mask = input_39_end_mask_0, x = y_11)[name = tensor("input_39")]; + tensor x_23 = elu(alpha = var_507, x = input_39)[name = tensor("x_23")]; + tensor input_41_interleave_0 = const()[name = tensor("input_41_interleave_0"), val = tensor(false)]; + tensor input_41 = concat(axis = var_508, interleave = input_41_interleave_0, values = (res1_conv0_prev, x_23))[name = tensor("input_41")]; + tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; + tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; + tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; + tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; + tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; + tensor input_43 = conv(bias = mimi_decoder_model_6_block_1_conv_bias, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = mimi_decoder_model_6_block_1_conv_weight, x = input_41)[name = tensor("input_43")]; + tensor var_641_begin_0 = const()[name = tensor("op_641_begin_0"), val = tensor([0, 0, 480])]; + tensor var_641_end_0 = const()[name = tensor("op_641_end_0"), val = tensor([1, 128, 482])]; + tensor var_641_end_mask_0 = const()[name = tensor("op_641_end_mask_0"), val = tensor([true, true, true])]; + tensor var_641 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, x = input_41)[name = tensor("op_641")]; + tensor x_25 = elu(alpha = var_507, x = input_43)[name = tensor("x_25")]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("valid")]; + tensor v_7_strides_0 = const()[name = tensor("v_7_strides_0"), val = tensor([1])]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0])]; + tensor v_7_dilations_0 = const()[name = tensor("v_7_dilations_0"), val = tensor([1])]; + tensor v_7_groups_0 = const()[name = tensor("v_7_groups_0"), val = tensor(1)]; + tensor v_7 = conv(bias = mimi_decoder_model_6_block_3_conv_bias, dilations = v_7_dilations_0, groups = v_7_groups_0, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = v_7_strides_0, weight = mimi_decoder_model_6_block_3_conv_weight, x = x_25)[name = tensor("v_7")]; + tensor input_45 = add(x = input_39, y = v_7)[name = tensor("input_45")]; + tensor input_47 = elu(alpha = var_507, x = input_45)[name = tensor("input_47")]; + tensor y_13_pad_type_0 = const()[name = tensor("y_13_pad_type_0"), val = tensor("valid")]; + tensor y_13_strides_0 = const()[name = tensor("y_13_strides_0"), val = tensor([4])]; + tensor y_13_pad_0 = const()[name = tensor("y_13_pad_0"), val = tensor([0, 0])]; + tensor y_13_dilations_0 = const()[name = tensor("y_13_dilations_0"), val = tensor([1])]; + tensor y_13_groups_0 = const()[name = tensor("y_13_groups_0"), val = tensor(1)]; + tensor y_13_has_output_shape_output_shape_0 = const()[name = tensor("y_13_has_output_shape_output_shape_0"), val = tensor([1, 64, 1924])]; + tensor y_13_has_output_shape = conv_transpose(bias = mimi_decoder_model_8_convtr_bias, dilations = y_13_dilations_0, groups = y_13_groups_0, output_shape = y_13_has_output_shape_output_shape_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = mimi_decoder_model_8_convtr_weight, x = input_47)[name = tensor("y_13_has_output_shape")]; + tensor var_669_begin_0 = const()[name = tensor("op_669_begin_0"), val = tensor([0, 0, 0])]; + tensor var_669_end_0 = const()[name = tensor("op_669_end_0"), val = tensor([1, 64, 4])]; + tensor var_669_end_mask_0 = const()[name = tensor("op_669_end_mask_0"), val = tensor([true, true, false])]; + tensor var_669 = slice_by_index(begin = var_669_begin_0, end = var_669_end_0, end_mask = var_669_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_669")]; + tensor var_670 = add(x = var_669, y = convtr2_partial)[name = tensor("op_670")]; + tensor var_671_begin_0 = const()[name = tensor("op_671_begin_0"), val = tensor([0, 0, 4])]; + tensor var_671_end_0 = const()[name = tensor("op_671_end_0"), val = tensor([1, 64, 1924])]; + tensor var_671_end_mask_0 = const()[name = tensor("op_671_end_mask_0"), val = tensor([true, true, true])]; + tensor var_671 = slice_by_index(begin = var_671_begin_0, end = var_671_end_0, end_mask = var_671_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_671")]; + tensor y_interleave_0 = const()[name = tensor("y_interleave_0"), val = tensor(false)]; + tensor y = concat(axis = var_508, interleave = y_interleave_0, values = (var_670, var_671))[name = tensor("y")]; + tensor new_partial_begin_0 = const()[name = tensor("new_partial_begin_0"), val = tensor([0, 0, 1920])]; + tensor new_partial_end_0 = const()[name = tensor("new_partial_end_0"), val = tensor([1, 64, 1924])]; + tensor new_partial_end_mask_0 = const()[name = tensor("new_partial_end_mask_0"), val = tensor([true, true, true])]; + tensor new_partial = slice_by_index(begin = new_partial_begin_0, end = new_partial_end_0, end_mask = new_partial_end_mask_0, x = y)[name = tensor("new_partial")]; + tensor var_676 = const()[name = tensor("op_676"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41767936)))]; + tensor var_677 = sub(x = new_partial, y = var_676)[name = tensor("op_677")]; + tensor input_49_begin_0 = const()[name = tensor("input_49_begin_0"), val = tensor([0, 0, 0])]; + tensor input_49_end_0 = const()[name = tensor("input_49_end_0"), val = tensor([1, 64, 1920])]; + tensor input_49_end_mask_0 = const()[name = tensor("input_49_end_mask_0"), val = tensor([true, true, false])]; + tensor input_49 = slice_by_index(begin = input_49_begin_0, end = input_49_end_0, end_mask = input_49_end_mask_0, x = y)[name = tensor("input_49")]; + tensor x_27 = elu(alpha = var_507, x = input_49)[name = tensor("x_27")]; + tensor input_51_interleave_0 = const()[name = tensor("input_51_interleave_0"), val = tensor(false)]; + tensor input_51 = concat(axis = var_508, interleave = input_51_interleave_0, values = (res2_conv0_prev, x_27))[name = tensor("input_51")]; + tensor input_53_pad_type_0 = const()[name = tensor("input_53_pad_type_0"), val = tensor("valid")]; + tensor input_53_strides_0 = const()[name = tensor("input_53_strides_0"), val = tensor([1])]; + tensor input_53_pad_0 = const()[name = tensor("input_53_pad_0"), val = tensor([0, 0])]; + tensor input_53_dilations_0 = const()[name = tensor("input_53_dilations_0"), val = tensor([1])]; + tensor input_53_groups_0 = const()[name = tensor("input_53_groups_0"), val = tensor(1)]; + tensor input_53 = conv(bias = mimi_decoder_model_9_block_1_conv_bias, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = mimi_decoder_model_9_block_1_conv_weight, x = input_51)[name = tensor("input_53")]; + tensor var_697_begin_0 = const()[name = tensor("op_697_begin_0"), val = tensor([0, 0, 1920])]; + tensor var_697_end_0 = const()[name = tensor("op_697_end_0"), val = tensor([1, 64, 1922])]; + tensor var_697_end_mask_0 = const()[name = tensor("op_697_end_mask_0"), val = tensor([true, true, true])]; + tensor var_697 = slice_by_index(begin = var_697_begin_0, end = var_697_end_0, end_mask = var_697_end_mask_0, x = input_51)[name = tensor("op_697")]; + tensor x_29 = elu(alpha = var_507, x = input_53)[name = tensor("x_29")]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("valid")]; + tensor v_strides_0 = const()[name = tensor("v_strides_0"), val = tensor([1])]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0])]; + tensor v_dilations_0 = const()[name = tensor("v_dilations_0"), val = tensor([1])]; + tensor v_groups_0 = const()[name = tensor("v_groups_0"), val = tensor(1)]; + tensor v = conv(bias = mimi_decoder_model_9_block_3_conv_bias, dilations = v_dilations_0, groups = v_groups_0, pad = v_pad_0, pad_type = v_pad_type_0, strides = v_strides_0, weight = mimi_decoder_model_9_block_3_conv_weight, x = x_29)[name = tensor("v")]; + tensor input_55 = add(x = input_49, y = v)[name = tensor("input_55")]; + tensor x = elu(alpha = var_507, x = input_55)[name = tensor("x")]; + tensor input_interleave_0 = const()[name = tensor("input_interleave_0"), val = tensor(false)]; + tensor input = concat(axis = var_508, interleave = input_interleave_0, values = (conv_final_prev, x))[name = tensor("input")]; + tensor var_724_pad_type_0 = const()[name = tensor("op_724_pad_type_0"), val = tensor("valid")]; + tensor var_724_strides_0 = const()[name = tensor("op_724_strides_0"), val = tensor([1])]; + tensor var_724_pad_0 = const()[name = tensor("op_724_pad_0"), val = tensor([0, 0])]; + tensor var_724_dilations_0 = const()[name = tensor("op_724_dilations_0"), val = tensor([1])]; + tensor var_724_groups_0 = const()[name = tensor("op_724_groups_0"), val = tensor(1)]; + tensor var_724 = conv(bias = mimi_decoder_model_11_conv_bias, dilations = var_724_dilations_0, groups = var_724_groups_0, pad = var_724_pad_0, pad_type = var_724_pad_type_0, strides = var_724_strides_0, weight = mimi_decoder_model_11_conv_weight, x = input)[name = tensor("op_724")]; + tensor var_725_begin_0 = const()[name = tensor("op_725_begin_0"), val = tensor([0, 0, 1920])]; + tensor var_725_end_0 = const()[name = tensor("op_725_end_0"), val = tensor([1, 64, 1922])]; + tensor var_725_end_mask_0 = const()[name = tensor("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = input)[name = tensor("op_725")]; + tensor var_740_promoted = const()[name = tensor("op_740_promoted"), val = tensor(0x1p+4)]; + tensor var_741 = add(x = attn0_offset, y = var_740_promoted)[name = tensor("op_741")]; + tensor var_743_promoted = const()[name = tensor("op_743_promoted"), val = tensor(0x1p+4)]; + tensor var_744 = add(x = attn1_offset, y = var_743_promoted)[name = tensor("op_744")]; + tensor conv0_first_tmp = identity(x = conv0_first)[name = tensor("conv0_first_tmp")]; + tensor res0_conv0_first_tmp = identity(x = res0_conv0_first)[name = tensor("res0_conv0_first_tmp")]; + tensor res0_conv1_prev_tmp = identity(x = res0_conv1_prev)[name = tensor("res0_conv1_prev_tmp")]; + tensor res0_conv1_first_tmp = identity(x = res0_conv1_first)[name = tensor("res0_conv1_first_tmp")]; + tensor res1_conv0_first_tmp = identity(x = res1_conv0_first)[name = tensor("res1_conv0_first_tmp")]; + tensor res1_conv1_prev_tmp = identity(x = res1_conv1_prev)[name = tensor("res1_conv1_prev_tmp")]; + tensor res1_conv1_first_tmp = identity(x = res1_conv1_first)[name = tensor("res1_conv1_first_tmp")]; + tensor res2_conv0_first_tmp = identity(x = res2_conv0_first)[name = tensor("res2_conv0_first_tmp")]; + tensor res2_conv1_prev_tmp = identity(x = res2_conv1_prev)[name = tensor("res2_conv1_prev_tmp")]; + tensor res2_conv1_first_tmp = identity(x = res2_conv1_first)[name = tensor("res2_conv1_first_tmp")]; + tensor conv_final_first_tmp = identity(x = conv_final_first)[name = tensor("conv_final_first_tmp")]; + } -> (var_724, var_77, var_210, var_741, var_400, var_744, var_542, conv0_first, var_565, var_585, res0_conv0_first, res0_conv1_prev, res0_conv1_first, var_621, var_641, res1_conv0_first, res1_conv1_prev, res1_conv1_first, var_677, var_697, res2_conv0_first, res2_conv1_prev, res2_conv1_first, var_725, conv_final_first); +} \ No newline at end of file diff --git a/v2/french_24l/mimi_decoder.mlmodelc/weights/weight.bin b/v2/french_24l/mimi_decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..bf37b9e4f126187f3237bb76752d699c30997f93 --- /dev/null +++ b/v2/french_24l/mimi_decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06dbd8b6f8fd4ffefd443aa5bb8b1927cb6d71f7bbac1117ada13938964d2b0b +size 41768256 diff --git a/v2/french_24l/mimi_decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/v2/french_24l/mimi_decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..1ce9c1af0059c79e89e547ac21f66ffb02b089ae --- /dev/null +++ b/v2/french_24l/mimi_decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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