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[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.30.0"}, {"coremlc-version", "1839.0.0"}})]
{
func main<ios16>(tensor<fp32, [1, 77]> input_ids) {
tensor<int32, []> var_5 = const()[name = tensor<string, []>("op_5"), val = tensor<int32, []>(-1)];
tensor<bool, []> var_6 = const()[name = tensor<string, []>("op_6"), val = tensor<bool, []>(false)];
tensor<string, []> cast_1_dtype_0 = const()[name = tensor<string, []>("cast_1_dtype_0"), val = tensor<string, []>("int32")];
tensor<int32, []> inputs_embeds_axis_0 = const()[name = tensor<string, []>("inputs_embeds_axis_0"), val = tensor<int32, []>(0)];
tensor<int32, []> inputs_embeds_batch_dims_0 = const()[name = tensor<string, []>("inputs_embeds_batch_dims_0"), val = tensor<int32, []>(0)];
tensor<fp16, [49408, 1024]> text_encoder_text_model_embeddings_token_embedding_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_embeddings_token_embedding_weight_to_fp16"), val = tensor<fp16, [49408, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
tensor<int32, [1, 77]> cast_2 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor<string, []>("cast_2")];
tensor<fp16, [1, 77, 1024]> inputs_embeds_cast = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_2, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor<string, []>("inputs_embeds_cast")];
tensor<fp16, [1, 77, 1024]> position_embeddings_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [59136]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101187712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101246912))), name = tensor<string, []>("position_embeddings_to_fp16_palettized"), shape = tensor<uint32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 1024]> input_3_cast = add(x = inputs_embeds_cast, y = position_embeddings_to_fp16_palettized)[name = tensor<string, []>("input_3_cast")];
tensor<int32, [1]> hidden_states_1_axes_0 = const()[name = tensor<string, []>("hidden_states_1_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101247104)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101249216)))];
tensor<fp16, []> var_12_to_fp16 = const()[name = tensor<string, []>("op_12_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 77, 1024]> hidden_states_1_cast = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast)[name = tensor<string, []>("hidden_states_1_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101251328))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102037824))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102038016)))];
tensor<fp16, [1, 77, 1024]> var_109_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("op_109_cast")];
tensor<fp16, []> var_110_to_fp16 = const()[name = tensor<string, []>("op_110_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_5_cast = mul(x = var_109_cast, y = var_110_to_fp16)[name = tensor<string, []>("tensor_5_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102040128))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102826624))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102826816)))];
tensor<fp16, [1, 77, 1024]> tensor_1_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("tensor_1_cast")];
tensor<int32, [4]> var_115 = const()[name = tensor<string, []>("op_115"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_116_cast = reshape(shape = var_115, x = tensor_1_cast)[name = tensor<string, []>("op_116_cast")];
tensor<int32, [4]> var_117_perm_0 = const()[name = tensor<string, []>("op_117_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102828928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103615424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103615616)))];
tensor<fp16, [1, 77, 1024]> tensor_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("tensor_3_cast")];
tensor<int32, [4]> var_122 = const()[name = tensor<string, []>("op_122"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_123_cast = reshape(shape = var_122, x = tensor_3_cast)[name = tensor<string, []>("op_123_cast")];
tensor<int32, [4]> var_124_perm_0 = const()[name = tensor<string, []>("op_124_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_131 = const()[name = tensor<string, []>("op_131"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_132_cast = reshape(shape = var_131, x = tensor_5_cast)[name = tensor<string, []>("op_132_cast")];
tensor<int32, [4]> var_133_perm_0 = const()[name = tensor<string, []>("op_133_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_114 = transpose(perm = var_133_perm_0, x = var_132_cast)[name = tensor<string, []>("transpose_114")];
tensor<fp16, [16, 77, 64]> query_states_1_cast = reshape(shape = var_135, x = transpose_114)[name = tensor<string, []>("query_states_1_cast")];
tensor<int32, [3]> var_137 = const()[name = tensor<string, []>("op_137"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_113 = transpose(perm = var_117_perm_0, x = var_116_cast)[name = tensor<string, []>("transpose_113")];
tensor<fp16, [16, 77, 64]> key_states_3_cast = reshape(shape = var_137, x = transpose_113)[name = tensor<string, []>("key_states_3_cast")];
tensor<int32, [3]> var_139 = const()[name = tensor<string, []>("op_139"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_112 = transpose(perm = var_124_perm_0, x = var_123_cast)[name = tensor<string, []>("transpose_112")];
tensor<fp16, [16, 77, 64]> value_states_3_cast = reshape(shape = var_139, x = transpose_112)[name = tensor<string, []>("value_states_3_cast")];
tensor<int32, [3]> var_142_perm_0 = const()[name = tensor<string, []>("op_142_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_1_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_1_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_111 = transpose(perm = var_142_perm_0, x = key_states_3_cast)[name = tensor<string, []>("transpose_111")];
tensor<fp16, [16, 77, 77]> attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = query_states_1_cast, y = transpose_111)[name = tensor<string, []>("attn_weights_1_cast")];
tensor<int32, [4]> var_144 = const()[name = tensor<string, []>("op_144"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_145_cast = reshape(shape = var_144, x = attn_weights_1_cast)[name = tensor<string, []>("op_145_cast")];
tensor<fp16, [1, 1, 77, 77]> causal_attention_mask_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4447]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103617728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103622272))), name = tensor<string, []>("causal_attention_mask_to_fp16_palettized"), shape = tensor<uint32, [4]>([1, 1, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> attn_weights_3_cast = add(x = var_145_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_3_cast")];
tensor<int32, [3]> var_150 = const()[name = tensor<string, []>("op_150"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_5_cast = reshape(shape = var_150, x = attn_weights_3_cast)[name = tensor<string, []>("input_5_cast")];
tensor<fp16, [16, 77, 77]> input_7_cast = softmax(axis = var_5, x = input_5_cast)[name = tensor<string, []>("input_7_cast")];
tensor<bool, []> attn_output_1_transpose_x_0 = const()[name = tensor<string, []>("attn_output_1_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_1_transpose_y_0 = const()[name = tensor<string, []>("attn_output_1_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_1_cast = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_7_cast, y = value_states_3_cast)[name = tensor<string, []>("attn_output_1_cast")];
tensor<int32, [4]> var_155 = const()[name = tensor<string, []>("op_155"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_3_cast = reshape(shape = var_155, x = attn_output_1_cast)[name = tensor<string, []>("attn_output_3_cast")];
tensor<int32, [4]> attn_output_5_perm_0 = const()[name = tensor<string, []>("attn_output_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_158 = const()[name = tensor<string, []>("op_158"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_110 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast)[name = tensor<string, []>("transpose_110")];
tensor<fp16, [1, 77, 1024]> input_9_cast = reshape(shape = var_158, x = transpose_110)[name = tensor<string, []>("input_9_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103622464))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104408960))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104409152)))];
tensor<fp16, [1, 77, 1024]> hidden_states_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized, x = input_9_cast)[name = tensor<string, []>("hidden_states_3_cast")];
tensor<fp16, [1, 77, 1024]> input_11_cast = add(x = input_3_cast, y = hidden_states_3_cast)[name = tensor<string, []>("input_11_cast")];
tensor<int32, [1]> input_13_axes_0 = const()[name = tensor<string, []>("input_13_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104411264)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104413376)))];
tensor<fp16, [1, 77, 1024]> input_13_cast = layer_norm(axes = input_13_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_11_cast)[name = tensor<string, []>("input_13_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104415488))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107561280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107561472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107564608))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_15_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_13_cast)[name = tensor<string, []>("input_15_cast")];
tensor<string, []> input_17_mode_0 = const()[name = tensor<string, []>("input_17_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_17_cast = gelu(mode = input_17_mode_0, x = input_15_cast)[name = tensor<string, []>("input_17_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107564800))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110710592))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110710784)))];
tensor<fp16, [1, 77, 1024]> hidden_states_5_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized, x = input_17_cast)[name = tensor<string, []>("hidden_states_5_cast")];
tensor<fp16, [1, 77, 1024]> input_19_cast = add(x = input_11_cast, y = hidden_states_5_cast)[name = tensor<string, []>("input_19_cast")];
tensor<int32, [1]> hidden_states_7_axes_0 = const()[name = tensor<string, []>("hidden_states_7_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110712896)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110715008)))];
tensor<fp16, [1, 77, 1024]> hidden_states_7_cast = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_19_cast)[name = tensor<string, []>("hidden_states_7_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110717120))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111503616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111503808)))];
tensor<fp16, [1, 77, 1024]> var_196_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("op_196_cast")];
tensor<fp16, []> var_197_to_fp16 = const()[name = tensor<string, []>("op_197_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_11_cast = mul(x = var_196_cast, y = var_197_to_fp16)[name = tensor<string, []>("tensor_11_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111505920))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112292416))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112292608)))];
tensor<fp16, [1, 77, 1024]> tensor_7_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("tensor_7_cast")];
tensor<int32, [4]> var_202 = const()[name = tensor<string, []>("op_202"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_203_cast = reshape(shape = var_202, x = tensor_7_cast)[name = tensor<string, []>("op_203_cast")];
tensor<int32, [4]> var_204_perm_0 = const()[name = tensor<string, []>("op_204_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112294720))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113081216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113081408)))];
tensor<fp16, [1, 77, 1024]> tensor_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("tensor_9_cast")];
tensor<int32, [4]> var_209 = const()[name = tensor<string, []>("op_209"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_210_cast = reshape(shape = var_209, x = tensor_9_cast)[name = tensor<string, []>("op_210_cast")];
tensor<int32, [4]> var_211_perm_0 = const()[name = tensor<string, []>("op_211_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_218 = const()[name = tensor<string, []>("op_218"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_219_cast = reshape(shape = var_218, x = tensor_11_cast)[name = tensor<string, []>("op_219_cast")];
tensor<int32, [4]> var_220_perm_0 = const()[name = tensor<string, []>("op_220_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_222 = const()[name = tensor<string, []>("op_222"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_109 = transpose(perm = var_220_perm_0, x = var_219_cast)[name = tensor<string, []>("transpose_109")];
tensor<fp16, [16, 77, 64]> query_states_3_cast = reshape(shape = var_222, x = transpose_109)[name = tensor<string, []>("query_states_3_cast")];
tensor<int32, [3]> var_224 = const()[name = tensor<string, []>("op_224"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_108 = transpose(perm = var_204_perm_0, x = var_203_cast)[name = tensor<string, []>("transpose_108")];
tensor<fp16, [16, 77, 64]> key_states_7_cast = reshape(shape = var_224, x = transpose_108)[name = tensor<string, []>("key_states_7_cast")];
tensor<int32, [3]> var_226 = const()[name = tensor<string, []>("op_226"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_107 = transpose(perm = var_211_perm_0, x = var_210_cast)[name = tensor<string, []>("transpose_107")];
tensor<fp16, [16, 77, 64]> value_states_7_cast = reshape(shape = var_226, x = transpose_107)[name = tensor<string, []>("value_states_7_cast")];
tensor<int32, [3]> var_229_perm_0 = const()[name = tensor<string, []>("op_229_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_7_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_7_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_7_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_7_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_106 = transpose(perm = var_229_perm_0, x = key_states_7_cast)[name = tensor<string, []>("transpose_106")];
tensor<fp16, [16, 77, 77]> attn_weights_7_cast = matmul(transpose_x = attn_weights_7_transpose_x_0, transpose_y = attn_weights_7_transpose_y_0, x = query_states_3_cast, y = transpose_106)[name = tensor<string, []>("attn_weights_7_cast")];
tensor<int32, [4]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_232_cast = reshape(shape = var_231, x = attn_weights_7_cast)[name = tensor<string, []>("op_232_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_9_cast = add(x = var_232_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_9_cast")];
tensor<int32, [3]> var_237 = const()[name = tensor<string, []>("op_237"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_21_cast = reshape(shape = var_237, x = attn_weights_9_cast)[name = tensor<string, []>("input_21_cast")];
tensor<fp16, [16, 77, 77]> input_23_cast = softmax(axis = var_5, x = input_21_cast)[name = tensor<string, []>("input_23_cast")];
tensor<bool, []> attn_output_7_transpose_x_0 = const()[name = tensor<string, []>("attn_output_7_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_7_transpose_y_0 = const()[name = tensor<string, []>("attn_output_7_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_7_cast = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_23_cast, y = value_states_7_cast)[name = tensor<string, []>("attn_output_7_cast")];
tensor<int32, [4]> var_242 = const()[name = tensor<string, []>("op_242"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_9_cast = reshape(shape = var_242, x = attn_output_7_cast)[name = tensor<string, []>("attn_output_9_cast")];
tensor<int32, [4]> attn_output_11_perm_0 = const()[name = tensor<string, []>("attn_output_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_245 = const()[name = tensor<string, []>("op_245"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_105 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast)[name = tensor<string, []>("transpose_105")];
tensor<fp16, [1, 77, 1024]> input_25_cast = reshape(shape = var_245, x = transpose_105)[name = tensor<string, []>("input_25_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113083520))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113870016))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113870208)))];
tensor<fp16, [1, 77, 1024]> hidden_states_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized, x = input_25_cast)[name = tensor<string, []>("hidden_states_9_cast")];
tensor<fp16, [1, 77, 1024]> input_27_cast = add(x = input_19_cast, y = hidden_states_9_cast)[name = tensor<string, []>("input_27_cast")];
tensor<int32, [1]> input_29_axes_0 = const()[name = tensor<string, []>("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113872320)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113874432)))];
tensor<fp16, [1, 77, 1024]> input_29_cast = layer_norm(axes = input_29_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_27_cast)[name = tensor<string, []>("input_29_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113876544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117022336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117022528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117025664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_31_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_29_cast)[name = tensor<string, []>("input_31_cast")];
tensor<string, []> input_33_mode_0 = const()[name = tensor<string, []>("input_33_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_33_cast = gelu(mode = input_33_mode_0, x = input_31_cast)[name = tensor<string, []>("input_33_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117025856))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120171648))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120171840)))];
tensor<fp16, [1, 77, 1024]> hidden_states_11_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized, x = input_33_cast)[name = tensor<string, []>("hidden_states_11_cast")];
tensor<fp16, [1, 77, 1024]> input_35_cast = add(x = input_27_cast, y = hidden_states_11_cast)[name = tensor<string, []>("input_35_cast")];
tensor<int32, [1]> hidden_states_13_axes_0 = const()[name = tensor<string, []>("hidden_states_13_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120173952)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120176064)))];
tensor<fp16, [1, 77, 1024]> hidden_states_13_cast = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_35_cast)[name = tensor<string, []>("hidden_states_13_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120178176))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120964672))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120964864)))];
tensor<fp16, [1, 77, 1024]> var_283_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("op_283_cast")];
tensor<fp16, []> var_284_to_fp16 = const()[name = tensor<string, []>("op_284_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_17_cast = mul(x = var_283_cast, y = var_284_to_fp16)[name = tensor<string, []>("tensor_17_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120966976))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121753472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121753664)))];
tensor<fp16, [1, 77, 1024]> tensor_13_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("tensor_13_cast")];
tensor<int32, [4]> var_289 = const()[name = tensor<string, []>("op_289"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_290_cast = reshape(shape = var_289, x = tensor_13_cast)[name = tensor<string, []>("op_290_cast")];
tensor<int32, [4]> var_291_perm_0 = const()[name = tensor<string, []>("op_291_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121755776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122542272))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122542464)))];
tensor<fp16, [1, 77, 1024]> tensor_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("tensor_15_cast")];
tensor<int32, [4]> var_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_297_cast = reshape(shape = var_296, x = tensor_15_cast)[name = tensor<string, []>("op_297_cast")];
tensor<int32, [4]> var_298_perm_0 = const()[name = tensor<string, []>("op_298_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_305 = const()[name = tensor<string, []>("op_305"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_306_cast = reshape(shape = var_305, x = tensor_17_cast)[name = tensor<string, []>("op_306_cast")];
tensor<int32, [4]> var_307_perm_0 = const()[name = tensor<string, []>("op_307_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_104 = transpose(perm = var_307_perm_0, x = var_306_cast)[name = tensor<string, []>("transpose_104")];
tensor<fp16, [16, 77, 64]> query_states_5_cast = reshape(shape = var_309, x = transpose_104)[name = tensor<string, []>("query_states_5_cast")];
tensor<int32, [3]> var_311 = const()[name = tensor<string, []>("op_311"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_103 = transpose(perm = var_291_perm_0, x = var_290_cast)[name = tensor<string, []>("transpose_103")];
tensor<fp16, [16, 77, 64]> key_states_11_cast = reshape(shape = var_311, x = transpose_103)[name = tensor<string, []>("key_states_11_cast")];
tensor<int32, [3]> var_313 = const()[name = tensor<string, []>("op_313"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_102 = transpose(perm = var_298_perm_0, x = var_297_cast)[name = tensor<string, []>("transpose_102")];
tensor<fp16, [16, 77, 64]> value_states_11_cast = reshape(shape = var_313, x = transpose_102)[name = tensor<string, []>("value_states_11_cast")];
tensor<int32, [3]> var_316_perm_0 = const()[name = tensor<string, []>("op_316_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_13_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_13_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_101 = transpose(perm = var_316_perm_0, x = key_states_11_cast)[name = tensor<string, []>("transpose_101")];
tensor<fp16, [16, 77, 77]> attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = query_states_5_cast, y = transpose_101)[name = tensor<string, []>("attn_weights_13_cast")];
tensor<int32, [4]> var_318 = const()[name = tensor<string, []>("op_318"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_319_cast = reshape(shape = var_318, x = attn_weights_13_cast)[name = tensor<string, []>("op_319_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_15_cast = add(x = var_319_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_15_cast")];
tensor<int32, [3]> var_324 = const()[name = tensor<string, []>("op_324"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_37_cast = reshape(shape = var_324, x = attn_weights_15_cast)[name = tensor<string, []>("input_37_cast")];
tensor<fp16, [16, 77, 77]> input_39_cast = softmax(axis = var_5, x = input_37_cast)[name = tensor<string, []>("input_39_cast")];
tensor<bool, []> attn_output_13_transpose_x_0 = const()[name = tensor<string, []>("attn_output_13_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_13_transpose_y_0 = const()[name = tensor<string, []>("attn_output_13_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_13_cast = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_39_cast, y = value_states_11_cast)[name = tensor<string, []>("attn_output_13_cast")];
tensor<int32, [4]> var_329 = const()[name = tensor<string, []>("op_329"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_15_cast = reshape(shape = var_329, x = attn_output_13_cast)[name = tensor<string, []>("attn_output_15_cast")];
tensor<int32, [4]> attn_output_17_perm_0 = const()[name = tensor<string, []>("attn_output_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_332 = const()[name = tensor<string, []>("op_332"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_100 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast)[name = tensor<string, []>("transpose_100")];
tensor<fp16, [1, 77, 1024]> input_41_cast = reshape(shape = var_332, x = transpose_100)[name = tensor<string, []>("input_41_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122544576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123331072))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123331264)))];
tensor<fp16, [1, 77, 1024]> hidden_states_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized, x = input_41_cast)[name = tensor<string, []>("hidden_states_15_cast")];
tensor<fp16, [1, 77, 1024]> input_43_cast = add(x = input_35_cast, y = hidden_states_15_cast)[name = tensor<string, []>("input_43_cast")];
tensor<int32, [1]> input_45_axes_0 = const()[name = tensor<string, []>("input_45_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123333376)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123335488)))];
tensor<fp16, [1, 77, 1024]> input_45_cast = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_43_cast)[name = tensor<string, []>("input_45_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123337600))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126483392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126483584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126486720))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_47_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_45_cast)[name = tensor<string, []>("input_47_cast")];
tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_49_cast = gelu(mode = input_49_mode_0, x = input_47_cast)[name = tensor<string, []>("input_49_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126486912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129632704))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129632896)))];
tensor<fp16, [1, 77, 1024]> hidden_states_17_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized, x = input_49_cast)[name = tensor<string, []>("hidden_states_17_cast")];
tensor<fp16, [1, 77, 1024]> input_51_cast = add(x = input_43_cast, y = hidden_states_17_cast)[name = tensor<string, []>("input_51_cast")];
tensor<int32, [1]> hidden_states_19_axes_0 = const()[name = tensor<string, []>("hidden_states_19_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129635008)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129637120)))];
tensor<fp16, [1, 77, 1024]> hidden_states_19_cast = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_51_cast)[name = tensor<string, []>("hidden_states_19_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129639232))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130425728))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130425920)))];
tensor<fp16, [1, 77, 1024]> var_370_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("op_370_cast")];
tensor<fp16, []> var_371_to_fp16 = const()[name = tensor<string, []>("op_371_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_23_cast = mul(x = var_370_cast, y = var_371_to_fp16)[name = tensor<string, []>("tensor_23_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130428032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131214528))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131214720)))];
tensor<fp16, [1, 77, 1024]> tensor_19_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("tensor_19_cast")];
tensor<int32, [4]> var_376 = const()[name = tensor<string, []>("op_376"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_377_cast = reshape(shape = var_376, x = tensor_19_cast)[name = tensor<string, []>("op_377_cast")];
tensor<int32, [4]> var_378_perm_0 = const()[name = tensor<string, []>("op_378_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131216832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132003328))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132003520)))];
tensor<fp16, [1, 77, 1024]> tensor_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("tensor_21_cast")];
tensor<int32, [4]> var_383 = const()[name = tensor<string, []>("op_383"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_384_cast = reshape(shape = var_383, x = tensor_21_cast)[name = tensor<string, []>("op_384_cast")];
tensor<int32, [4]> var_385_perm_0 = const()[name = tensor<string, []>("op_385_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_392 = const()[name = tensor<string, []>("op_392"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_393_cast = reshape(shape = var_392, x = tensor_23_cast)[name = tensor<string, []>("op_393_cast")];
tensor<int32, [4]> var_394_perm_0 = const()[name = tensor<string, []>("op_394_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_396 = const()[name = tensor<string, []>("op_396"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_99 = transpose(perm = var_394_perm_0, x = var_393_cast)[name = tensor<string, []>("transpose_99")];
tensor<fp16, [16, 77, 64]> query_states_7_cast = reshape(shape = var_396, x = transpose_99)[name = tensor<string, []>("query_states_7_cast")];
tensor<int32, [3]> var_398 = const()[name = tensor<string, []>("op_398"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_98 = transpose(perm = var_378_perm_0, x = var_377_cast)[name = tensor<string, []>("transpose_98")];
tensor<fp16, [16, 77, 64]> key_states_15_cast = reshape(shape = var_398, x = transpose_98)[name = tensor<string, []>("key_states_15_cast")];
tensor<int32, [3]> var_400 = const()[name = tensor<string, []>("op_400"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_97 = transpose(perm = var_385_perm_0, x = var_384_cast)[name = tensor<string, []>("transpose_97")];
tensor<fp16, [16, 77, 64]> value_states_15_cast = reshape(shape = var_400, x = transpose_97)[name = tensor<string, []>("value_states_15_cast")];
tensor<int32, [3]> var_403_perm_0 = const()[name = tensor<string, []>("op_403_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_19_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_19_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_19_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_19_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_96 = transpose(perm = var_403_perm_0, x = key_states_15_cast)[name = tensor<string, []>("transpose_96")];
tensor<fp16, [16, 77, 77]> attn_weights_19_cast = matmul(transpose_x = attn_weights_19_transpose_x_0, transpose_y = attn_weights_19_transpose_y_0, x = query_states_7_cast, y = transpose_96)[name = tensor<string, []>("attn_weights_19_cast")];
tensor<int32, [4]> var_405 = const()[name = tensor<string, []>("op_405"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_406_cast = reshape(shape = var_405, x = attn_weights_19_cast)[name = tensor<string, []>("op_406_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_21_cast = add(x = var_406_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_21_cast")];
tensor<int32, [3]> var_411 = const()[name = tensor<string, []>("op_411"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_53_cast = reshape(shape = var_411, x = attn_weights_21_cast)[name = tensor<string, []>("input_53_cast")];
tensor<fp16, [16, 77, 77]> input_55_cast = softmax(axis = var_5, x = input_53_cast)[name = tensor<string, []>("input_55_cast")];
tensor<bool, []> attn_output_19_transpose_x_0 = const()[name = tensor<string, []>("attn_output_19_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_19_transpose_y_0 = const()[name = tensor<string, []>("attn_output_19_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_19_cast = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_55_cast, y = value_states_15_cast)[name = tensor<string, []>("attn_output_19_cast")];
tensor<int32, [4]> var_416 = const()[name = tensor<string, []>("op_416"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_21_cast = reshape(shape = var_416, x = attn_output_19_cast)[name = tensor<string, []>("attn_output_21_cast")];
tensor<int32, [4]> attn_output_23_perm_0 = const()[name = tensor<string, []>("attn_output_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_419 = const()[name = tensor<string, []>("op_419"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_95 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast)[name = tensor<string, []>("transpose_95")];
tensor<fp16, [1, 77, 1024]> input_57_cast = reshape(shape = var_419, x = transpose_95)[name = tensor<string, []>("input_57_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132005632))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132792128))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132792320)))];
tensor<fp16, [1, 77, 1024]> hidden_states_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized, x = input_57_cast)[name = tensor<string, []>("hidden_states_21_cast")];
tensor<fp16, [1, 77, 1024]> input_59_cast = add(x = input_51_cast, y = hidden_states_21_cast)[name = tensor<string, []>("input_59_cast")];
tensor<int32, [1]> input_61_axes_0 = const()[name = tensor<string, []>("input_61_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132794432)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132796544)))];
tensor<fp16, [1, 77, 1024]> input_61_cast = layer_norm(axes = input_61_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_59_cast)[name = tensor<string, []>("input_61_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132798656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135944448))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135944640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135947776))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_63_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_61_cast)[name = tensor<string, []>("input_63_cast")];
tensor<string, []> input_65_mode_0 = const()[name = tensor<string, []>("input_65_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_65_cast = gelu(mode = input_65_mode_0, x = input_63_cast)[name = tensor<string, []>("input_65_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135947968))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139093760))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139093952)))];
tensor<fp16, [1, 77, 1024]> hidden_states_23_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized, x = input_65_cast)[name = tensor<string, []>("hidden_states_23_cast")];
tensor<fp16, [1, 77, 1024]> input_67_cast = add(x = input_59_cast, y = hidden_states_23_cast)[name = tensor<string, []>("input_67_cast")];
tensor<int32, [1]> hidden_states_25_axes_0 = const()[name = tensor<string, []>("hidden_states_25_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139096064)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139098176)))];
tensor<fp16, [1, 77, 1024]> hidden_states_25_cast = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_67_cast)[name = tensor<string, []>("hidden_states_25_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139100288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139886784))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139886976)))];
tensor<fp16, [1, 77, 1024]> var_457_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("op_457_cast")];
tensor<fp16, []> var_458_to_fp16 = const()[name = tensor<string, []>("op_458_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_29_cast = mul(x = var_457_cast, y = var_458_to_fp16)[name = tensor<string, []>("tensor_29_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139889088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140675584))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140675776)))];
tensor<fp16, [1, 77, 1024]> tensor_25_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("tensor_25_cast")];
tensor<int32, [4]> var_463 = const()[name = tensor<string, []>("op_463"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_464_cast = reshape(shape = var_463, x = tensor_25_cast)[name = tensor<string, []>("op_464_cast")];
tensor<int32, [4]> var_465_perm_0 = const()[name = tensor<string, []>("op_465_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140677888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141464384))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141464576)))];
tensor<fp16, [1, 77, 1024]> tensor_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("tensor_27_cast")];
tensor<int32, [4]> var_470 = const()[name = tensor<string, []>("op_470"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_471_cast = reshape(shape = var_470, x = tensor_27_cast)[name = tensor<string, []>("op_471_cast")];
tensor<int32, [4]> var_472_perm_0 = const()[name = tensor<string, []>("op_472_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_479 = const()[name = tensor<string, []>("op_479"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_480_cast = reshape(shape = var_479, x = tensor_29_cast)[name = tensor<string, []>("op_480_cast")];
tensor<int32, [4]> var_481_perm_0 = const()[name = tensor<string, []>("op_481_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_483 = const()[name = tensor<string, []>("op_483"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_94 = transpose(perm = var_481_perm_0, x = var_480_cast)[name = tensor<string, []>("transpose_94")];
tensor<fp16, [16, 77, 64]> query_states_9_cast = reshape(shape = var_483, x = transpose_94)[name = tensor<string, []>("query_states_9_cast")];
tensor<int32, [3]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_93 = transpose(perm = var_465_perm_0, x = var_464_cast)[name = tensor<string, []>("transpose_93")];
tensor<fp16, [16, 77, 64]> key_states_19_cast = reshape(shape = var_485, x = transpose_93)[name = tensor<string, []>("key_states_19_cast")];
tensor<int32, [3]> var_487 = const()[name = tensor<string, []>("op_487"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_92 = transpose(perm = var_472_perm_0, x = var_471_cast)[name = tensor<string, []>("transpose_92")];
tensor<fp16, [16, 77, 64]> value_states_19_cast = reshape(shape = var_487, x = transpose_92)[name = tensor<string, []>("value_states_19_cast")];
tensor<int32, [3]> var_490_perm_0 = const()[name = tensor<string, []>("op_490_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_25_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_25_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_91 = transpose(perm = var_490_perm_0, x = key_states_19_cast)[name = tensor<string, []>("transpose_91")];
tensor<fp16, [16, 77, 77]> attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = query_states_9_cast, y = transpose_91)[name = tensor<string, []>("attn_weights_25_cast")];
tensor<int32, [4]> var_492 = const()[name = tensor<string, []>("op_492"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_493_cast = reshape(shape = var_492, x = attn_weights_25_cast)[name = tensor<string, []>("op_493_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_27_cast = add(x = var_493_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_27_cast")];
tensor<int32, [3]> var_498 = const()[name = tensor<string, []>("op_498"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_69_cast = reshape(shape = var_498, x = attn_weights_27_cast)[name = tensor<string, []>("input_69_cast")];
tensor<fp16, [16, 77, 77]> input_71_cast = softmax(axis = var_5, x = input_69_cast)[name = tensor<string, []>("input_71_cast")];
tensor<bool, []> attn_output_25_transpose_x_0 = const()[name = tensor<string, []>("attn_output_25_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_25_transpose_y_0 = const()[name = tensor<string, []>("attn_output_25_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_25_cast = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_71_cast, y = value_states_19_cast)[name = tensor<string, []>("attn_output_25_cast")];
tensor<int32, [4]> var_503 = const()[name = tensor<string, []>("op_503"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_27_cast = reshape(shape = var_503, x = attn_output_25_cast)[name = tensor<string, []>("attn_output_27_cast")];
tensor<int32, [4]> attn_output_29_perm_0 = const()[name = tensor<string, []>("attn_output_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_506 = const()[name = tensor<string, []>("op_506"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_90 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast)[name = tensor<string, []>("transpose_90")];
tensor<fp16, [1, 77, 1024]> input_73_cast = reshape(shape = var_506, x = transpose_90)[name = tensor<string, []>("input_73_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141466688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142253184))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142253376)))];
tensor<fp16, [1, 77, 1024]> hidden_states_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized, x = input_73_cast)[name = tensor<string, []>("hidden_states_27_cast")];
tensor<fp16, [1, 77, 1024]> input_75_cast = add(x = input_67_cast, y = hidden_states_27_cast)[name = tensor<string, []>("input_75_cast")];
tensor<int32, [1]> input_77_axes_0 = const()[name = tensor<string, []>("input_77_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142255488)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142257600)))];
tensor<fp16, [1, 77, 1024]> input_77_cast = layer_norm(axes = input_77_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_75_cast)[name = tensor<string, []>("input_77_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142259712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145405504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145405696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145408832))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_79_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_77_cast)[name = tensor<string, []>("input_79_cast")];
tensor<string, []> input_81_mode_0 = const()[name = tensor<string, []>("input_81_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_81_cast = gelu(mode = input_81_mode_0, x = input_79_cast)[name = tensor<string, []>("input_81_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145409024))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148554816))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148555008)))];
tensor<fp16, [1, 77, 1024]> hidden_states_29_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized, x = input_81_cast)[name = tensor<string, []>("hidden_states_29_cast")];
tensor<fp16, [1, 77, 1024]> input_83_cast = add(x = input_75_cast, y = hidden_states_29_cast)[name = tensor<string, []>("input_83_cast")];
tensor<int32, [1]> hidden_states_31_axes_0 = const()[name = tensor<string, []>("hidden_states_31_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148557120)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148559232)))];
tensor<fp16, [1, 77, 1024]> hidden_states_31_cast = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_83_cast)[name = tensor<string, []>("hidden_states_31_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148561344))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149347840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149348032)))];
tensor<fp16, [1, 77, 1024]> var_544_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("op_544_cast")];
tensor<fp16, []> var_545_to_fp16 = const()[name = tensor<string, []>("op_545_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_35_cast = mul(x = var_544_cast, y = var_545_to_fp16)[name = tensor<string, []>("tensor_35_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149350144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150136640))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150136832)))];
tensor<fp16, [1, 77, 1024]> tensor_31_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("tensor_31_cast")];
tensor<int32, [4]> var_550 = const()[name = tensor<string, []>("op_550"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_551_cast = reshape(shape = var_550, x = tensor_31_cast)[name = tensor<string, []>("op_551_cast")];
tensor<int32, [4]> var_552_perm_0 = const()[name = tensor<string, []>("op_552_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150138944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150925440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150925632)))];
tensor<fp16, [1, 77, 1024]> tensor_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("tensor_33_cast")];
tensor<int32, [4]> var_557 = const()[name = tensor<string, []>("op_557"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_558_cast = reshape(shape = var_557, x = tensor_33_cast)[name = tensor<string, []>("op_558_cast")];
tensor<int32, [4]> var_559_perm_0 = const()[name = tensor<string, []>("op_559_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_566 = const()[name = tensor<string, []>("op_566"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_567_cast = reshape(shape = var_566, x = tensor_35_cast)[name = tensor<string, []>("op_567_cast")];
tensor<int32, [4]> var_568_perm_0 = const()[name = tensor<string, []>("op_568_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_570 = const()[name = tensor<string, []>("op_570"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_89 = transpose(perm = var_568_perm_0, x = var_567_cast)[name = tensor<string, []>("transpose_89")];
tensor<fp16, [16, 77, 64]> query_states_11_cast = reshape(shape = var_570, x = transpose_89)[name = tensor<string, []>("query_states_11_cast")];
tensor<int32, [3]> var_572 = const()[name = tensor<string, []>("op_572"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_88 = transpose(perm = var_552_perm_0, x = var_551_cast)[name = tensor<string, []>("transpose_88")];
tensor<fp16, [16, 77, 64]> key_states_23_cast = reshape(shape = var_572, x = transpose_88)[name = tensor<string, []>("key_states_23_cast")];
tensor<int32, [3]> var_574 = const()[name = tensor<string, []>("op_574"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_87 = transpose(perm = var_559_perm_0, x = var_558_cast)[name = tensor<string, []>("transpose_87")];
tensor<fp16, [16, 77, 64]> value_states_23_cast = reshape(shape = var_574, x = transpose_87)[name = tensor<string, []>("value_states_23_cast")];
tensor<int32, [3]> var_577_perm_0 = const()[name = tensor<string, []>("op_577_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_31_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_31_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_31_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_31_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_86 = transpose(perm = var_577_perm_0, x = key_states_23_cast)[name = tensor<string, []>("transpose_86")];
tensor<fp16, [16, 77, 77]> attn_weights_31_cast = matmul(transpose_x = attn_weights_31_transpose_x_0, transpose_y = attn_weights_31_transpose_y_0, x = query_states_11_cast, y = transpose_86)[name = tensor<string, []>("attn_weights_31_cast")];
tensor<int32, [4]> var_579 = const()[name = tensor<string, []>("op_579"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_580_cast = reshape(shape = var_579, x = attn_weights_31_cast)[name = tensor<string, []>("op_580_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_33_cast = add(x = var_580_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_33_cast")];
tensor<int32, [3]> var_585 = const()[name = tensor<string, []>("op_585"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_85_cast = reshape(shape = var_585, x = attn_weights_33_cast)[name = tensor<string, []>("input_85_cast")];
tensor<fp16, [16, 77, 77]> input_87_cast = softmax(axis = var_5, x = input_85_cast)[name = tensor<string, []>("input_87_cast")];
tensor<bool, []> attn_output_31_transpose_x_0 = const()[name = tensor<string, []>("attn_output_31_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_31_transpose_y_0 = const()[name = tensor<string, []>("attn_output_31_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_31_cast = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_87_cast, y = value_states_23_cast)[name = tensor<string, []>("attn_output_31_cast")];
tensor<int32, [4]> var_590 = const()[name = tensor<string, []>("op_590"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_33_cast = reshape(shape = var_590, x = attn_output_31_cast)[name = tensor<string, []>("attn_output_33_cast")];
tensor<int32, [4]> attn_output_35_perm_0 = const()[name = tensor<string, []>("attn_output_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_593 = const()[name = tensor<string, []>("op_593"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_85 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast)[name = tensor<string, []>("transpose_85")];
tensor<fp16, [1, 77, 1024]> input_89_cast = reshape(shape = var_593, x = transpose_85)[name = tensor<string, []>("input_89_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150927744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151714240))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151714432)))];
tensor<fp16, [1, 77, 1024]> hidden_states_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized, x = input_89_cast)[name = tensor<string, []>("hidden_states_33_cast")];
tensor<fp16, [1, 77, 1024]> input_91_cast = add(x = input_83_cast, y = hidden_states_33_cast)[name = tensor<string, []>("input_91_cast")];
tensor<int32, [1]> input_93_axes_0 = const()[name = tensor<string, []>("input_93_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151716544)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151718656)))];
tensor<fp16, [1, 77, 1024]> input_93_cast = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_91_cast)[name = tensor<string, []>("input_93_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151720768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154866560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154866752))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154869888))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_95_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_93_cast)[name = tensor<string, []>("input_95_cast")];
tensor<string, []> input_97_mode_0 = const()[name = tensor<string, []>("input_97_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_97_cast = gelu(mode = input_97_mode_0, x = input_95_cast)[name = tensor<string, []>("input_97_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154870080))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158015872))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158016064)))];
tensor<fp16, [1, 77, 1024]> hidden_states_35_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized, x = input_97_cast)[name = tensor<string, []>("hidden_states_35_cast")];
tensor<fp16, [1, 77, 1024]> input_99_cast = add(x = input_91_cast, y = hidden_states_35_cast)[name = tensor<string, []>("input_99_cast")];
tensor<int32, [1]> hidden_states_37_axes_0 = const()[name = tensor<string, []>("hidden_states_37_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158018176)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158020288)))];
tensor<fp16, [1, 77, 1024]> hidden_states_37_cast = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_99_cast)[name = tensor<string, []>("hidden_states_37_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158022400))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158808896))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158809088)))];
tensor<fp16, [1, 77, 1024]> var_631_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("op_631_cast")];
tensor<fp16, []> var_632_to_fp16 = const()[name = tensor<string, []>("op_632_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_41_cast = mul(x = var_631_cast, y = var_632_to_fp16)[name = tensor<string, []>("tensor_41_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158811200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159597696))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159597888)))];
tensor<fp16, [1, 77, 1024]> tensor_37_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("tensor_37_cast")];
tensor<int32, [4]> var_637 = const()[name = tensor<string, []>("op_637"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_638_cast = reshape(shape = var_637, x = tensor_37_cast)[name = tensor<string, []>("op_638_cast")];
tensor<int32, [4]> var_639_perm_0 = const()[name = tensor<string, []>("op_639_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159600000))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160386496))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160386688)))];
tensor<fp16, [1, 77, 1024]> tensor_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("tensor_39_cast")];
tensor<int32, [4]> var_644 = const()[name = tensor<string, []>("op_644"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_645_cast = reshape(shape = var_644, x = tensor_39_cast)[name = tensor<string, []>("op_645_cast")];
tensor<int32, [4]> var_646_perm_0 = const()[name = tensor<string, []>("op_646_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_653 = const()[name = tensor<string, []>("op_653"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_654_cast = reshape(shape = var_653, x = tensor_41_cast)[name = tensor<string, []>("op_654_cast")];
tensor<int32, [4]> var_655_perm_0 = const()[name = tensor<string, []>("op_655_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_657 = const()[name = tensor<string, []>("op_657"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_84 = transpose(perm = var_655_perm_0, x = var_654_cast)[name = tensor<string, []>("transpose_84")];
tensor<fp16, [16, 77, 64]> query_states_13_cast = reshape(shape = var_657, x = transpose_84)[name = tensor<string, []>("query_states_13_cast")];
tensor<int32, [3]> var_659 = const()[name = tensor<string, []>("op_659"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_83 = transpose(perm = var_639_perm_0, x = var_638_cast)[name = tensor<string, []>("transpose_83")];
tensor<fp16, [16, 77, 64]> key_states_27_cast = reshape(shape = var_659, x = transpose_83)[name = tensor<string, []>("key_states_27_cast")];
tensor<int32, [3]> var_661 = const()[name = tensor<string, []>("op_661"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_82 = transpose(perm = var_646_perm_0, x = var_645_cast)[name = tensor<string, []>("transpose_82")];
tensor<fp16, [16, 77, 64]> value_states_27_cast = reshape(shape = var_661, x = transpose_82)[name = tensor<string, []>("value_states_27_cast")];
tensor<int32, [3]> var_664_perm_0 = const()[name = tensor<string, []>("op_664_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_37_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_37_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_81 = transpose(perm = var_664_perm_0, x = key_states_27_cast)[name = tensor<string, []>("transpose_81")];
tensor<fp16, [16, 77, 77]> attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = query_states_13_cast, y = transpose_81)[name = tensor<string, []>("attn_weights_37_cast")];
tensor<int32, [4]> var_666 = const()[name = tensor<string, []>("op_666"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_667_cast = reshape(shape = var_666, x = attn_weights_37_cast)[name = tensor<string, []>("op_667_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_39_cast = add(x = var_667_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_39_cast")];
tensor<int32, [3]> var_672 = const()[name = tensor<string, []>("op_672"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_101_cast = reshape(shape = var_672, x = attn_weights_39_cast)[name = tensor<string, []>("input_101_cast")];
tensor<fp16, [16, 77, 77]> input_103_cast = softmax(axis = var_5, x = input_101_cast)[name = tensor<string, []>("input_103_cast")];
tensor<bool, []> attn_output_37_transpose_x_0 = const()[name = tensor<string, []>("attn_output_37_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_37_transpose_y_0 = const()[name = tensor<string, []>("attn_output_37_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_37_cast = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = input_103_cast, y = value_states_27_cast)[name = tensor<string, []>("attn_output_37_cast")];
tensor<int32, [4]> var_677 = const()[name = tensor<string, []>("op_677"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_39_cast = reshape(shape = var_677, x = attn_output_37_cast)[name = tensor<string, []>("attn_output_39_cast")];
tensor<int32, [4]> attn_output_41_perm_0 = const()[name = tensor<string, []>("attn_output_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_680 = const()[name = tensor<string, []>("op_680"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_80 = transpose(perm = attn_output_41_perm_0, x = attn_output_39_cast)[name = tensor<string, []>("transpose_80")];
tensor<fp16, [1, 77, 1024]> input_105_cast = reshape(shape = var_680, x = transpose_80)[name = tensor<string, []>("input_105_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160388800))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161175296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161175488)))];
tensor<fp16, [1, 77, 1024]> hidden_states_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized, x = input_105_cast)[name = tensor<string, []>("hidden_states_39_cast")];
tensor<fp16, [1, 77, 1024]> input_107_cast = add(x = input_99_cast, y = hidden_states_39_cast)[name = tensor<string, []>("input_107_cast")];
tensor<int32, [1]> input_109_axes_0 = const()[name = tensor<string, []>("input_109_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161177600)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161179712)))];
tensor<fp16, [1, 77, 1024]> input_109_cast = layer_norm(axes = input_109_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_107_cast)[name = tensor<string, []>("input_109_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161181824))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164327616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164327808))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164330944))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_111_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_109_cast)[name = tensor<string, []>("input_111_cast")];
tensor<string, []> input_113_mode_0 = const()[name = tensor<string, []>("input_113_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_113_cast = gelu(mode = input_113_mode_0, x = input_111_cast)[name = tensor<string, []>("input_113_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164331136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167476928))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167477120)))];
tensor<fp16, [1, 77, 1024]> hidden_states_41_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized, x = input_113_cast)[name = tensor<string, []>("hidden_states_41_cast")];
tensor<fp16, [1, 77, 1024]> input_115_cast = add(x = input_107_cast, y = hidden_states_41_cast)[name = tensor<string, []>("input_115_cast")];
tensor<int32, [1]> hidden_states_43_axes_0 = const()[name = tensor<string, []>("hidden_states_43_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167479232)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167481344)))];
tensor<fp16, [1, 77, 1024]> hidden_states_43_cast = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_115_cast)[name = tensor<string, []>("hidden_states_43_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167483456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168269952))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168270144)))];
tensor<fp16, [1, 77, 1024]> var_718_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("op_718_cast")];
tensor<fp16, []> var_719_to_fp16 = const()[name = tensor<string, []>("op_719_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_47_cast = mul(x = var_718_cast, y = var_719_to_fp16)[name = tensor<string, []>("tensor_47_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168272256))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169058752))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169058944)))];
tensor<fp16, [1, 77, 1024]> tensor_43_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("tensor_43_cast")];
tensor<int32, [4]> var_724 = const()[name = tensor<string, []>("op_724"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_725_cast = reshape(shape = var_724, x = tensor_43_cast)[name = tensor<string, []>("op_725_cast")];
tensor<int32, [4]> var_726_perm_0 = const()[name = tensor<string, []>("op_726_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169061056))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169847552))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169847744)))];
tensor<fp16, [1, 77, 1024]> tensor_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("tensor_45_cast")];
tensor<int32, [4]> var_731 = const()[name = tensor<string, []>("op_731"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_732_cast = reshape(shape = var_731, x = tensor_45_cast)[name = tensor<string, []>("op_732_cast")];
tensor<int32, [4]> var_733_perm_0 = const()[name = tensor<string, []>("op_733_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_740 = const()[name = tensor<string, []>("op_740"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_741_cast = reshape(shape = var_740, x = tensor_47_cast)[name = tensor<string, []>("op_741_cast")];
tensor<int32, [4]> var_742_perm_0 = const()[name = tensor<string, []>("op_742_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_744 = const()[name = tensor<string, []>("op_744"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_79 = transpose(perm = var_742_perm_0, x = var_741_cast)[name = tensor<string, []>("transpose_79")];
tensor<fp16, [16, 77, 64]> query_states_15_cast = reshape(shape = var_744, x = transpose_79)[name = tensor<string, []>("query_states_15_cast")];
tensor<int32, [3]> var_746 = const()[name = tensor<string, []>("op_746"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_78 = transpose(perm = var_726_perm_0, x = var_725_cast)[name = tensor<string, []>("transpose_78")];
tensor<fp16, [16, 77, 64]> key_states_31_cast = reshape(shape = var_746, x = transpose_78)[name = tensor<string, []>("key_states_31_cast")];
tensor<int32, [3]> var_748 = const()[name = tensor<string, []>("op_748"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_77 = transpose(perm = var_733_perm_0, x = var_732_cast)[name = tensor<string, []>("transpose_77")];
tensor<fp16, [16, 77, 64]> value_states_31_cast = reshape(shape = var_748, x = transpose_77)[name = tensor<string, []>("value_states_31_cast")];
tensor<int32, [3]> var_751_perm_0 = const()[name = tensor<string, []>("op_751_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_43_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_43_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_43_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_43_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_76 = transpose(perm = var_751_perm_0, x = key_states_31_cast)[name = tensor<string, []>("transpose_76")];
tensor<fp16, [16, 77, 77]> attn_weights_43_cast = matmul(transpose_x = attn_weights_43_transpose_x_0, transpose_y = attn_weights_43_transpose_y_0, x = query_states_15_cast, y = transpose_76)[name = tensor<string, []>("attn_weights_43_cast")];
tensor<int32, [4]> var_753 = const()[name = tensor<string, []>("op_753"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_754_cast = reshape(shape = var_753, x = attn_weights_43_cast)[name = tensor<string, []>("op_754_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_45_cast = add(x = var_754_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_45_cast")];
tensor<int32, [3]> var_759 = const()[name = tensor<string, []>("op_759"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_117_cast = reshape(shape = var_759, x = attn_weights_45_cast)[name = tensor<string, []>("input_117_cast")];
tensor<fp16, [16, 77, 77]> input_119_cast = softmax(axis = var_5, x = input_117_cast)[name = tensor<string, []>("input_119_cast")];
tensor<bool, []> attn_output_43_transpose_x_0 = const()[name = tensor<string, []>("attn_output_43_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_43_transpose_y_0 = const()[name = tensor<string, []>("attn_output_43_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_43_cast = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = input_119_cast, y = value_states_31_cast)[name = tensor<string, []>("attn_output_43_cast")];
tensor<int32, [4]> var_764 = const()[name = tensor<string, []>("op_764"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_45_cast = reshape(shape = var_764, x = attn_output_43_cast)[name = tensor<string, []>("attn_output_45_cast")];
tensor<int32, [4]> attn_output_47_perm_0 = const()[name = tensor<string, []>("attn_output_47_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_767 = const()[name = tensor<string, []>("op_767"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_75 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast)[name = tensor<string, []>("transpose_75")];
tensor<fp16, [1, 77, 1024]> input_121_cast = reshape(shape = var_767, x = transpose_75)[name = tensor<string, []>("input_121_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169849856))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170636352))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170636544)))];
tensor<fp16, [1, 77, 1024]> hidden_states_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized, x = input_121_cast)[name = tensor<string, []>("hidden_states_45_cast")];
tensor<fp16, [1, 77, 1024]> input_123_cast = add(x = input_115_cast, y = hidden_states_45_cast)[name = tensor<string, []>("input_123_cast")];
tensor<int32, [1]> input_125_axes_0 = const()[name = tensor<string, []>("input_125_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170638656)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170640768)))];
tensor<fp16, [1, 77, 1024]> input_125_cast = layer_norm(axes = input_125_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_123_cast)[name = tensor<string, []>("input_125_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170642880))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173788672))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173788864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173792000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_127_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_125_cast)[name = tensor<string, []>("input_127_cast")];
tensor<string, []> input_129_mode_0 = const()[name = tensor<string, []>("input_129_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_129_cast = gelu(mode = input_129_mode_0, x = input_127_cast)[name = tensor<string, []>("input_129_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173792192))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176937984))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176938176)))];
tensor<fp16, [1, 77, 1024]> hidden_states_47_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized, x = input_129_cast)[name = tensor<string, []>("hidden_states_47_cast")];
tensor<fp16, [1, 77, 1024]> input_131_cast = add(x = input_123_cast, y = hidden_states_47_cast)[name = tensor<string, []>("input_131_cast")];
tensor<int32, [1]> hidden_states_49_axes_0 = const()[name = tensor<string, []>("hidden_states_49_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176940288)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176942400)))];
tensor<fp16, [1, 77, 1024]> hidden_states_49_cast = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_131_cast)[name = tensor<string, []>("hidden_states_49_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176944512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(177731008))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(177731200)))];
tensor<fp16, [1, 77, 1024]> var_805_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("op_805_cast")];
tensor<fp16, []> var_806_to_fp16 = const()[name = tensor<string, []>("op_806_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_53_cast = mul(x = var_805_cast, y = var_806_to_fp16)[name = tensor<string, []>("tensor_53_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(177733312))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178519808))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178520000)))];
tensor<fp16, [1, 77, 1024]> tensor_49_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("tensor_49_cast")];
tensor<int32, [4]> var_811 = const()[name = tensor<string, []>("op_811"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_812_cast = reshape(shape = var_811, x = tensor_49_cast)[name = tensor<string, []>("op_812_cast")];
tensor<int32, [4]> var_813_perm_0 = const()[name = tensor<string, []>("op_813_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178522112))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179308608))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179308800)))];
tensor<fp16, [1, 77, 1024]> tensor_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("tensor_51_cast")];
tensor<int32, [4]> var_818 = const()[name = tensor<string, []>("op_818"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_819_cast = reshape(shape = var_818, x = tensor_51_cast)[name = tensor<string, []>("op_819_cast")];
tensor<int32, [4]> var_820_perm_0 = const()[name = tensor<string, []>("op_820_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_827 = const()[name = tensor<string, []>("op_827"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_828_cast = reshape(shape = var_827, x = tensor_53_cast)[name = tensor<string, []>("op_828_cast")];
tensor<int32, [4]> var_829_perm_0 = const()[name = tensor<string, []>("op_829_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_831 = const()[name = tensor<string, []>("op_831"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_74 = transpose(perm = var_829_perm_0, x = var_828_cast)[name = tensor<string, []>("transpose_74")];
tensor<fp16, [16, 77, 64]> query_states_17_cast = reshape(shape = var_831, x = transpose_74)[name = tensor<string, []>("query_states_17_cast")];
tensor<int32, [3]> var_833 = const()[name = tensor<string, []>("op_833"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_73 = transpose(perm = var_813_perm_0, x = var_812_cast)[name = tensor<string, []>("transpose_73")];
tensor<fp16, [16, 77, 64]> key_states_35_cast = reshape(shape = var_833, x = transpose_73)[name = tensor<string, []>("key_states_35_cast")];
tensor<int32, [3]> var_835 = const()[name = tensor<string, []>("op_835"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_72 = transpose(perm = var_820_perm_0, x = var_819_cast)[name = tensor<string, []>("transpose_72")];
tensor<fp16, [16, 77, 64]> value_states_35_cast = reshape(shape = var_835, x = transpose_72)[name = tensor<string, []>("value_states_35_cast")];
tensor<int32, [3]> var_838_perm_0 = const()[name = tensor<string, []>("op_838_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_49_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_49_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_71 = transpose(perm = var_838_perm_0, x = key_states_35_cast)[name = tensor<string, []>("transpose_71")];
tensor<fp16, [16, 77, 77]> attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = query_states_17_cast, y = transpose_71)[name = tensor<string, []>("attn_weights_49_cast")];
tensor<int32, [4]> var_840 = const()[name = tensor<string, []>("op_840"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_841_cast = reshape(shape = var_840, x = attn_weights_49_cast)[name = tensor<string, []>("op_841_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_51_cast = add(x = var_841_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_51_cast")];
tensor<int32, [3]> var_846 = const()[name = tensor<string, []>("op_846"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_133_cast = reshape(shape = var_846, x = attn_weights_51_cast)[name = tensor<string, []>("input_133_cast")];
tensor<fp16, [16, 77, 77]> input_135_cast = softmax(axis = var_5, x = input_133_cast)[name = tensor<string, []>("input_135_cast")];
tensor<bool, []> attn_output_49_transpose_x_0 = const()[name = tensor<string, []>("attn_output_49_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_49_transpose_y_0 = const()[name = tensor<string, []>("attn_output_49_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_49_cast = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = input_135_cast, y = value_states_35_cast)[name = tensor<string, []>("attn_output_49_cast")];
tensor<int32, [4]> var_851 = const()[name = tensor<string, []>("op_851"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_51_cast = reshape(shape = var_851, x = attn_output_49_cast)[name = tensor<string, []>("attn_output_51_cast")];
tensor<int32, [4]> attn_output_53_perm_0 = const()[name = tensor<string, []>("attn_output_53_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_854 = const()[name = tensor<string, []>("op_854"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_70 = transpose(perm = attn_output_53_perm_0, x = attn_output_51_cast)[name = tensor<string, []>("transpose_70")];
tensor<fp16, [1, 77, 1024]> input_137_cast = reshape(shape = var_854, x = transpose_70)[name = tensor<string, []>("input_137_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179310912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180097408))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180097600)))];
tensor<fp16, [1, 77, 1024]> hidden_states_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized, x = input_137_cast)[name = tensor<string, []>("hidden_states_51_cast")];
tensor<fp16, [1, 77, 1024]> input_139_cast = add(x = input_131_cast, y = hidden_states_51_cast)[name = tensor<string, []>("input_139_cast")];
tensor<int32, [1]> input_141_axes_0 = const()[name = tensor<string, []>("input_141_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180099712)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180101824)))];
tensor<fp16, [1, 77, 1024]> input_141_cast = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_139_cast)[name = tensor<string, []>("input_141_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180103936))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183249728))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183249920))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183253056))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_143_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized, x = input_141_cast)[name = tensor<string, []>("input_143_cast")];
tensor<string, []> input_145_mode_0 = const()[name = tensor<string, []>("input_145_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_145_cast = gelu(mode = input_145_mode_0, x = input_143_cast)[name = tensor<string, []>("input_145_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183253248))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186399040))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186399232)))];
tensor<fp16, [1, 77, 1024]> hidden_states_53_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized, x = input_145_cast)[name = tensor<string, []>("hidden_states_53_cast")];
tensor<fp16, [1, 77, 1024]> input_147_cast = add(x = input_139_cast, y = hidden_states_53_cast)[name = tensor<string, []>("input_147_cast")];
tensor<int32, [1]> hidden_states_55_axes_0 = const()[name = tensor<string, []>("hidden_states_55_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186401344)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186403456)))];
tensor<fp16, [1, 77, 1024]> hidden_states_55_cast = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_147_cast)[name = tensor<string, []>("hidden_states_55_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186405568))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187192064))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187192256)))];
tensor<fp16, [1, 77, 1024]> var_892_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("op_892_cast")];
tensor<fp16, []> var_893_to_fp16 = const()[name = tensor<string, []>("op_893_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_59_cast = mul(x = var_892_cast, y = var_893_to_fp16)[name = tensor<string, []>("tensor_59_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187194368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187980864))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187981056)))];
tensor<fp16, [1, 77, 1024]> tensor_55_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("tensor_55_cast")];
tensor<int32, [4]> var_898 = const()[name = tensor<string, []>("op_898"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_899_cast = reshape(shape = var_898, x = tensor_55_cast)[name = tensor<string, []>("op_899_cast")];
tensor<int32, [4]> var_900_perm_0 = const()[name = tensor<string, []>("op_900_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187983168))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188769664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188769856)))];
tensor<fp16, [1, 77, 1024]> tensor_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("tensor_57_cast")];
tensor<int32, [4]> var_905 = const()[name = tensor<string, []>("op_905"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_906_cast = reshape(shape = var_905, x = tensor_57_cast)[name = tensor<string, []>("op_906_cast")];
tensor<int32, [4]> var_907_perm_0 = const()[name = tensor<string, []>("op_907_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_914 = const()[name = tensor<string, []>("op_914"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_915_cast = reshape(shape = var_914, x = tensor_59_cast)[name = tensor<string, []>("op_915_cast")];
tensor<int32, [4]> var_916_perm_0 = const()[name = tensor<string, []>("op_916_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_918 = const()[name = tensor<string, []>("op_918"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_69 = transpose(perm = var_916_perm_0, x = var_915_cast)[name = tensor<string, []>("transpose_69")];
tensor<fp16, [16, 77, 64]> query_states_19_cast = reshape(shape = var_918, x = transpose_69)[name = tensor<string, []>("query_states_19_cast")];
tensor<int32, [3]> var_920 = const()[name = tensor<string, []>("op_920"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_68 = transpose(perm = var_900_perm_0, x = var_899_cast)[name = tensor<string, []>("transpose_68")];
tensor<fp16, [16, 77, 64]> key_states_39_cast = reshape(shape = var_920, x = transpose_68)[name = tensor<string, []>("key_states_39_cast")];
tensor<int32, [3]> var_922 = const()[name = tensor<string, []>("op_922"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_67 = transpose(perm = var_907_perm_0, x = var_906_cast)[name = tensor<string, []>("transpose_67")];
tensor<fp16, [16, 77, 64]> value_states_39_cast = reshape(shape = var_922, x = transpose_67)[name = tensor<string, []>("value_states_39_cast")];
tensor<int32, [3]> var_925_perm_0 = const()[name = tensor<string, []>("op_925_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_55_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_55_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_55_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_55_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_66 = transpose(perm = var_925_perm_0, x = key_states_39_cast)[name = tensor<string, []>("transpose_66")];
tensor<fp16, [16, 77, 77]> attn_weights_55_cast = matmul(transpose_x = attn_weights_55_transpose_x_0, transpose_y = attn_weights_55_transpose_y_0, x = query_states_19_cast, y = transpose_66)[name = tensor<string, []>("attn_weights_55_cast")];
tensor<int32, [4]> var_927 = const()[name = tensor<string, []>("op_927"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_928_cast = reshape(shape = var_927, x = attn_weights_55_cast)[name = tensor<string, []>("op_928_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_57_cast = add(x = var_928_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_57_cast")];
tensor<int32, [3]> var_933 = const()[name = tensor<string, []>("op_933"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_149_cast = reshape(shape = var_933, x = attn_weights_57_cast)[name = tensor<string, []>("input_149_cast")];
tensor<fp16, [16, 77, 77]> input_151_cast = softmax(axis = var_5, x = input_149_cast)[name = tensor<string, []>("input_151_cast")];
tensor<bool, []> attn_output_55_transpose_x_0 = const()[name = tensor<string, []>("attn_output_55_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_55_transpose_y_0 = const()[name = tensor<string, []>("attn_output_55_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_55_cast = matmul(transpose_x = attn_output_55_transpose_x_0, transpose_y = attn_output_55_transpose_y_0, x = input_151_cast, y = value_states_39_cast)[name = tensor<string, []>("attn_output_55_cast")];
tensor<int32, [4]> var_938 = const()[name = tensor<string, []>("op_938"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_57_cast = reshape(shape = var_938, x = attn_output_55_cast)[name = tensor<string, []>("attn_output_57_cast")];
tensor<int32, [4]> attn_output_59_perm_0 = const()[name = tensor<string, []>("attn_output_59_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_941 = const()[name = tensor<string, []>("op_941"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_65 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast)[name = tensor<string, []>("transpose_65")];
tensor<fp16, [1, 77, 1024]> input_153_cast = reshape(shape = var_941, x = transpose_65)[name = tensor<string, []>("input_153_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188771968))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189558464))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189558656)))];
tensor<fp16, [1, 77, 1024]> hidden_states_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized, x = input_153_cast)[name = tensor<string, []>("hidden_states_57_cast")];
tensor<fp16, [1, 77, 1024]> input_155_cast = add(x = input_147_cast, y = hidden_states_57_cast)[name = tensor<string, []>("input_155_cast")];
tensor<int32, [1]> input_157_axes_0 = const()[name = tensor<string, []>("input_157_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189560768)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189562880)))];
tensor<fp16, [1, 77, 1024]> input_157_cast = layer_norm(axes = input_157_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_155_cast)[name = tensor<string, []>("input_157_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189564992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192710784))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192710976))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192714112))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_159_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized, x = input_157_cast)[name = tensor<string, []>("input_159_cast")];
tensor<string, []> input_161_mode_0 = const()[name = tensor<string, []>("input_161_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_161_cast = gelu(mode = input_161_mode_0, x = input_159_cast)[name = tensor<string, []>("input_161_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192714304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195860096))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195860288)))];
tensor<fp16, [1, 77, 1024]> hidden_states_59_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized, x = input_161_cast)[name = tensor<string, []>("hidden_states_59_cast")];
tensor<fp16, [1, 77, 1024]> input_163_cast = add(x = input_155_cast, y = hidden_states_59_cast)[name = tensor<string, []>("input_163_cast")];
tensor<int32, [1]> hidden_states_61_axes_0 = const()[name = tensor<string, []>("hidden_states_61_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195862400)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195864512)))];
tensor<fp16, [1, 77, 1024]> hidden_states_61_cast = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_163_cast)[name = tensor<string, []>("hidden_states_61_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195866624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196653120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196653312)))];
tensor<fp16, [1, 77, 1024]> var_979_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("op_979_cast")];
tensor<fp16, []> var_980_to_fp16 = const()[name = tensor<string, []>("op_980_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_65_cast = mul(x = var_979_cast, y = var_980_to_fp16)[name = tensor<string, []>("tensor_65_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196655424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197441920))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197442112)))];
tensor<fp16, [1, 77, 1024]> tensor_61_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("tensor_61_cast")];
tensor<int32, [4]> var_985 = const()[name = tensor<string, []>("op_985"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_986_cast = reshape(shape = var_985, x = tensor_61_cast)[name = tensor<string, []>("op_986_cast")];
tensor<int32, [4]> var_987_perm_0 = const()[name = tensor<string, []>("op_987_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197444224))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198230720))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198230912)))];
tensor<fp16, [1, 77, 1024]> tensor_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("tensor_63_cast")];
tensor<int32, [4]> var_992 = const()[name = tensor<string, []>("op_992"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_993_cast = reshape(shape = var_992, x = tensor_63_cast)[name = tensor<string, []>("op_993_cast")];
tensor<int32, [4]> var_994_perm_0 = const()[name = tensor<string, []>("op_994_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1001 = const()[name = tensor<string, []>("op_1001"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1002_cast = reshape(shape = var_1001, x = tensor_65_cast)[name = tensor<string, []>("op_1002_cast")];
tensor<int32, [4]> var_1003_perm_0 = const()[name = tensor<string, []>("op_1003_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1005 = const()[name = tensor<string, []>("op_1005"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_64 = transpose(perm = var_1003_perm_0, x = var_1002_cast)[name = tensor<string, []>("transpose_64")];
tensor<fp16, [16, 77, 64]> query_states_21_cast = reshape(shape = var_1005, x = transpose_64)[name = tensor<string, []>("query_states_21_cast")];
tensor<int32, [3]> var_1007 = const()[name = tensor<string, []>("op_1007"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_63 = transpose(perm = var_987_perm_0, x = var_986_cast)[name = tensor<string, []>("transpose_63")];
tensor<fp16, [16, 77, 64]> key_states_43_cast = reshape(shape = var_1007, x = transpose_63)[name = tensor<string, []>("key_states_43_cast")];
tensor<int32, [3]> var_1009 = const()[name = tensor<string, []>("op_1009"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_62 = transpose(perm = var_994_perm_0, x = var_993_cast)[name = tensor<string, []>("transpose_62")];
tensor<fp16, [16, 77, 64]> value_states_43_cast = reshape(shape = var_1009, x = transpose_62)[name = tensor<string, []>("value_states_43_cast")];
tensor<int32, [3]> var_1012_perm_0 = const()[name = tensor<string, []>("op_1012_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_61_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_61_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_61 = transpose(perm = var_1012_perm_0, x = key_states_43_cast)[name = tensor<string, []>("transpose_61")];
tensor<fp16, [16, 77, 77]> attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = query_states_21_cast, y = transpose_61)[name = tensor<string, []>("attn_weights_61_cast")];
tensor<int32, [4]> var_1014 = const()[name = tensor<string, []>("op_1014"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1015_cast = reshape(shape = var_1014, x = attn_weights_61_cast)[name = tensor<string, []>("op_1015_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_63_cast = add(x = var_1015_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_63_cast")];
tensor<int32, [3]> var_1020 = const()[name = tensor<string, []>("op_1020"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_165_cast = reshape(shape = var_1020, x = attn_weights_63_cast)[name = tensor<string, []>("input_165_cast")];
tensor<fp16, [16, 77, 77]> input_167_cast = softmax(axis = var_5, x = input_165_cast)[name = tensor<string, []>("input_167_cast")];
tensor<bool, []> attn_output_61_transpose_x_0 = const()[name = tensor<string, []>("attn_output_61_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_61_transpose_y_0 = const()[name = tensor<string, []>("attn_output_61_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_61_cast = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = input_167_cast, y = value_states_43_cast)[name = tensor<string, []>("attn_output_61_cast")];
tensor<int32, [4]> var_1025 = const()[name = tensor<string, []>("op_1025"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_63_cast = reshape(shape = var_1025, x = attn_output_61_cast)[name = tensor<string, []>("attn_output_63_cast")];
tensor<int32, [4]> attn_output_65_perm_0 = const()[name = tensor<string, []>("attn_output_65_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1028 = const()[name = tensor<string, []>("op_1028"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_60 = transpose(perm = attn_output_65_perm_0, x = attn_output_63_cast)[name = tensor<string, []>("transpose_60")];
tensor<fp16, [1, 77, 1024]> input_169_cast = reshape(shape = var_1028, x = transpose_60)[name = tensor<string, []>("input_169_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198233024))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199019520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199019712)))];
tensor<fp16, [1, 77, 1024]> hidden_states_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized, x = input_169_cast)[name = tensor<string, []>("hidden_states_63_cast")];
tensor<fp16, [1, 77, 1024]> input_171_cast = add(x = input_163_cast, y = hidden_states_63_cast)[name = tensor<string, []>("input_171_cast")];
tensor<int32, [1]> input_173_axes_0 = const()[name = tensor<string, []>("input_173_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199021824)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199023936)))];
tensor<fp16, [1, 77, 1024]> input_173_cast = layer_norm(axes = input_173_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_171_cast)[name = tensor<string, []>("input_173_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199026048))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202171840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202172032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202175168))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_175_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized, x = input_173_cast)[name = tensor<string, []>("input_175_cast")];
tensor<string, []> input_177_mode_0 = const()[name = tensor<string, []>("input_177_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_177_cast = gelu(mode = input_177_mode_0, x = input_175_cast)[name = tensor<string, []>("input_177_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202175360))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205321152))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205321344)))];
tensor<fp16, [1, 77, 1024]> hidden_states_65_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized, x = input_177_cast)[name = tensor<string, []>("hidden_states_65_cast")];
tensor<fp16, [1, 77, 1024]> input_179_cast = add(x = input_171_cast, y = hidden_states_65_cast)[name = tensor<string, []>("input_179_cast")];
tensor<int32, [1]> hidden_states_67_axes_0 = const()[name = tensor<string, []>("hidden_states_67_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205323456)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205325568)))];
tensor<fp16, [1, 77, 1024]> hidden_states_67_cast = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_179_cast)[name = tensor<string, []>("hidden_states_67_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205327680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206114176))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206114368)))];
tensor<fp16, [1, 77, 1024]> var_1066_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("op_1066_cast")];
tensor<fp16, []> var_1067_to_fp16 = const()[name = tensor<string, []>("op_1067_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_71_cast = mul(x = var_1066_cast, y = var_1067_to_fp16)[name = tensor<string, []>("tensor_71_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206116480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206902976))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206903168)))];
tensor<fp16, [1, 77, 1024]> tensor_67_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("tensor_67_cast")];
tensor<int32, [4]> var_1072 = const()[name = tensor<string, []>("op_1072"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1073_cast = reshape(shape = var_1072, x = tensor_67_cast)[name = tensor<string, []>("op_1073_cast")];
tensor<int32, [4]> var_1074_perm_0 = const()[name = tensor<string, []>("op_1074_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206905280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207691776))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207691968)))];
tensor<fp16, [1, 77, 1024]> tensor_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("tensor_69_cast")];
tensor<int32, [4]> var_1079 = const()[name = tensor<string, []>("op_1079"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1080_cast = reshape(shape = var_1079, x = tensor_69_cast)[name = tensor<string, []>("op_1080_cast")];
tensor<int32, [4]> var_1081_perm_0 = const()[name = tensor<string, []>("op_1081_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1088 = const()[name = tensor<string, []>("op_1088"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1089_cast = reshape(shape = var_1088, x = tensor_71_cast)[name = tensor<string, []>("op_1089_cast")];
tensor<int32, [4]> var_1090_perm_0 = const()[name = tensor<string, []>("op_1090_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1092 = const()[name = tensor<string, []>("op_1092"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_59 = transpose(perm = var_1090_perm_0, x = var_1089_cast)[name = tensor<string, []>("transpose_59")];
tensor<fp16, [16, 77, 64]> query_states_23_cast = reshape(shape = var_1092, x = transpose_59)[name = tensor<string, []>("query_states_23_cast")];
tensor<int32, [3]> var_1094 = const()[name = tensor<string, []>("op_1094"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_58 = transpose(perm = var_1074_perm_0, x = var_1073_cast)[name = tensor<string, []>("transpose_58")];
tensor<fp16, [16, 77, 64]> key_states_47_cast = reshape(shape = var_1094, x = transpose_58)[name = tensor<string, []>("key_states_47_cast")];
tensor<int32, [3]> var_1096 = const()[name = tensor<string, []>("op_1096"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_57 = transpose(perm = var_1081_perm_0, x = var_1080_cast)[name = tensor<string, []>("transpose_57")];
tensor<fp16, [16, 77, 64]> value_states_47_cast = reshape(shape = var_1096, x = transpose_57)[name = tensor<string, []>("value_states_47_cast")];
tensor<int32, [3]> var_1099_perm_0 = const()[name = tensor<string, []>("op_1099_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_67_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_67_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_67_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_67_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_56 = transpose(perm = var_1099_perm_0, x = key_states_47_cast)[name = tensor<string, []>("transpose_56")];
tensor<fp16, [16, 77, 77]> attn_weights_67_cast = matmul(transpose_x = attn_weights_67_transpose_x_0, transpose_y = attn_weights_67_transpose_y_0, x = query_states_23_cast, y = transpose_56)[name = tensor<string, []>("attn_weights_67_cast")];
tensor<int32, [4]> var_1101 = const()[name = tensor<string, []>("op_1101"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1102_cast = reshape(shape = var_1101, x = attn_weights_67_cast)[name = tensor<string, []>("op_1102_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_69_cast = add(x = var_1102_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_69_cast")];
tensor<int32, [3]> var_1107 = const()[name = tensor<string, []>("op_1107"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_181_cast = reshape(shape = var_1107, x = attn_weights_69_cast)[name = tensor<string, []>("input_181_cast")];
tensor<fp16, [16, 77, 77]> input_183_cast = softmax(axis = var_5, x = input_181_cast)[name = tensor<string, []>("input_183_cast")];
tensor<bool, []> attn_output_67_transpose_x_0 = const()[name = tensor<string, []>("attn_output_67_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_67_transpose_y_0 = const()[name = tensor<string, []>("attn_output_67_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_67_cast = matmul(transpose_x = attn_output_67_transpose_x_0, transpose_y = attn_output_67_transpose_y_0, x = input_183_cast, y = value_states_47_cast)[name = tensor<string, []>("attn_output_67_cast")];
tensor<int32, [4]> var_1112 = const()[name = tensor<string, []>("op_1112"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_69_cast = reshape(shape = var_1112, x = attn_output_67_cast)[name = tensor<string, []>("attn_output_69_cast")];
tensor<int32, [4]> attn_output_71_perm_0 = const()[name = tensor<string, []>("attn_output_71_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1115 = const()[name = tensor<string, []>("op_1115"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_55 = transpose(perm = attn_output_71_perm_0, x = attn_output_69_cast)[name = tensor<string, []>("transpose_55")];
tensor<fp16, [1, 77, 1024]> input_185_cast = reshape(shape = var_1115, x = transpose_55)[name = tensor<string, []>("input_185_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207694080))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208480576))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208480768)))];
tensor<fp16, [1, 77, 1024]> hidden_states_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized, x = input_185_cast)[name = tensor<string, []>("hidden_states_69_cast")];
tensor<fp16, [1, 77, 1024]> input_187_cast = add(x = input_179_cast, y = hidden_states_69_cast)[name = tensor<string, []>("input_187_cast")];
tensor<int32, [1]> input_189_axes_0 = const()[name = tensor<string, []>("input_189_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208482880)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208484992)))];
tensor<fp16, [1, 77, 1024]> input_189_cast = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_187_cast)[name = tensor<string, []>("input_189_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208487104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211632896))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211633088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211636224))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_191_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized, x = input_189_cast)[name = tensor<string, []>("input_191_cast")];
tensor<string, []> input_193_mode_0 = const()[name = tensor<string, []>("input_193_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_193_cast = gelu(mode = input_193_mode_0, x = input_191_cast)[name = tensor<string, []>("input_193_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211636416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214782208))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214782400)))];
tensor<fp16, [1, 77, 1024]> hidden_states_71_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized, x = input_193_cast)[name = tensor<string, []>("hidden_states_71_cast")];
tensor<fp16, [1, 77, 1024]> input_195_cast = add(x = input_187_cast, y = hidden_states_71_cast)[name = tensor<string, []>("input_195_cast")];
tensor<int32, [1]> hidden_states_73_axes_0 = const()[name = tensor<string, []>("hidden_states_73_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214784512)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214786624)))];
tensor<fp16, [1, 77, 1024]> hidden_states_73_cast = layer_norm(axes = hidden_states_73_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16, x = input_195_cast)[name = tensor<string, []>("hidden_states_73_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214788736))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215575232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215575424)))];
tensor<fp16, [1, 77, 1024]> var_1153_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("op_1153_cast")];
tensor<fp16, []> var_1154_to_fp16 = const()[name = tensor<string, []>("op_1154_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_77_cast = mul(x = var_1153_cast, y = var_1154_to_fp16)[name = tensor<string, []>("tensor_77_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215577536))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216364032))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216364224)))];
tensor<fp16, [1, 77, 1024]> tensor_73_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("tensor_73_cast")];
tensor<int32, [4]> var_1159 = const()[name = tensor<string, []>("op_1159"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1160_cast = reshape(shape = var_1159, x = tensor_73_cast)[name = tensor<string, []>("op_1160_cast")];
tensor<int32, [4]> var_1161_perm_0 = const()[name = tensor<string, []>("op_1161_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216366336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217152832))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217153024)))];
tensor<fp16, [1, 77, 1024]> tensor_75_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("tensor_75_cast")];
tensor<int32, [4]> var_1166 = const()[name = tensor<string, []>("op_1166"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1167_cast = reshape(shape = var_1166, x = tensor_75_cast)[name = tensor<string, []>("op_1167_cast")];
tensor<int32, [4]> var_1168_perm_0 = const()[name = tensor<string, []>("op_1168_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1175 = const()[name = tensor<string, []>("op_1175"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1176_cast = reshape(shape = var_1175, x = tensor_77_cast)[name = tensor<string, []>("op_1176_cast")];
tensor<int32, [4]> var_1177_perm_0 = const()[name = tensor<string, []>("op_1177_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1179 = const()[name = tensor<string, []>("op_1179"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_54 = transpose(perm = var_1177_perm_0, x = var_1176_cast)[name = tensor<string, []>("transpose_54")];
tensor<fp16, [16, 77, 64]> query_states_25_cast = reshape(shape = var_1179, x = transpose_54)[name = tensor<string, []>("query_states_25_cast")];
tensor<int32, [3]> var_1181 = const()[name = tensor<string, []>("op_1181"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_53 = transpose(perm = var_1161_perm_0, x = var_1160_cast)[name = tensor<string, []>("transpose_53")];
tensor<fp16, [16, 77, 64]> key_states_51_cast = reshape(shape = var_1181, x = transpose_53)[name = tensor<string, []>("key_states_51_cast")];
tensor<int32, [3]> var_1183 = const()[name = tensor<string, []>("op_1183"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_52 = transpose(perm = var_1168_perm_0, x = var_1167_cast)[name = tensor<string, []>("transpose_52")];
tensor<fp16, [16, 77, 64]> value_states_51_cast = reshape(shape = var_1183, x = transpose_52)[name = tensor<string, []>("value_states_51_cast")];
tensor<int32, [3]> var_1186_perm_0 = const()[name = tensor<string, []>("op_1186_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_73_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_73_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_51 = transpose(perm = var_1186_perm_0, x = key_states_51_cast)[name = tensor<string, []>("transpose_51")];
tensor<fp16, [16, 77, 77]> attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = query_states_25_cast, y = transpose_51)[name = tensor<string, []>("attn_weights_73_cast")];
tensor<int32, [4]> var_1188 = const()[name = tensor<string, []>("op_1188"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1189_cast = reshape(shape = var_1188, x = attn_weights_73_cast)[name = tensor<string, []>("op_1189_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_75_cast = add(x = var_1189_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_75_cast")];
tensor<int32, [3]> var_1194 = const()[name = tensor<string, []>("op_1194"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_197_cast = reshape(shape = var_1194, x = attn_weights_75_cast)[name = tensor<string, []>("input_197_cast")];
tensor<fp16, [16, 77, 77]> input_199_cast = softmax(axis = var_5, x = input_197_cast)[name = tensor<string, []>("input_199_cast")];
tensor<bool, []> attn_output_73_transpose_x_0 = const()[name = tensor<string, []>("attn_output_73_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_73_transpose_y_0 = const()[name = tensor<string, []>("attn_output_73_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_73_cast = matmul(transpose_x = attn_output_73_transpose_x_0, transpose_y = attn_output_73_transpose_y_0, x = input_199_cast, y = value_states_51_cast)[name = tensor<string, []>("attn_output_73_cast")];
tensor<int32, [4]> var_1199 = const()[name = tensor<string, []>("op_1199"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_75_cast = reshape(shape = var_1199, x = attn_output_73_cast)[name = tensor<string, []>("attn_output_75_cast")];
tensor<int32, [4]> attn_output_77_perm_0 = const()[name = tensor<string, []>("attn_output_77_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1202 = const()[name = tensor<string, []>("op_1202"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_50 = transpose(perm = attn_output_77_perm_0, x = attn_output_75_cast)[name = tensor<string, []>("transpose_50")];
tensor<fp16, [1, 77, 1024]> input_201_cast = reshape(shape = var_1202, x = transpose_50)[name = tensor<string, []>("input_201_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217155136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217941632))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217941824)))];
tensor<fp16, [1, 77, 1024]> hidden_states_75_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized, x = input_201_cast)[name = tensor<string, []>("hidden_states_75_cast")];
tensor<fp16, [1, 77, 1024]> input_203_cast = add(x = input_195_cast, y = hidden_states_75_cast)[name = tensor<string, []>("input_203_cast")];
tensor<int32, [1]> input_205_axes_0 = const()[name = tensor<string, []>("input_205_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217943936)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217946048)))];
tensor<fp16, [1, 77, 1024]> input_205_cast = layer_norm(axes = input_205_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16, x = input_203_cast)[name = tensor<string, []>("input_205_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217948160))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(221093952))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(221094144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(221097280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_207_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized, x = input_205_cast)[name = tensor<string, []>("input_207_cast")];
tensor<string, []> input_209_mode_0 = const()[name = tensor<string, []>("input_209_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_209_cast = gelu(mode = input_209_mode_0, x = input_207_cast)[name = tensor<string, []>("input_209_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(221097472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224243264))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224243456)))];
tensor<fp16, [1, 77, 1024]> hidden_states_77_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized, x = input_209_cast)[name = tensor<string, []>("hidden_states_77_cast")];
tensor<fp16, [1, 77, 1024]> input_211_cast = add(x = input_203_cast, y = hidden_states_77_cast)[name = tensor<string, []>("input_211_cast")];
tensor<int32, [1]> hidden_states_79_axes_0 = const()[name = tensor<string, []>("hidden_states_79_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224245568)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224247680)))];
tensor<fp16, [1, 77, 1024]> hidden_states_79_cast = layer_norm(axes = hidden_states_79_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16, x = input_211_cast)[name = tensor<string, []>("hidden_states_79_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224249792))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225036288))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225036480)))];
tensor<fp16, [1, 77, 1024]> var_1240_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("op_1240_cast")];
tensor<fp16, []> var_1241_to_fp16 = const()[name = tensor<string, []>("op_1241_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_83_cast = mul(x = var_1240_cast, y = var_1241_to_fp16)[name = tensor<string, []>("tensor_83_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225038592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225825088))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225825280)))];
tensor<fp16, [1, 77, 1024]> tensor_79_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("tensor_79_cast")];
tensor<int32, [4]> var_1246 = const()[name = tensor<string, []>("op_1246"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1247_cast = reshape(shape = var_1246, x = tensor_79_cast)[name = tensor<string, []>("op_1247_cast")];
tensor<int32, [4]> var_1248_perm_0 = const()[name = tensor<string, []>("op_1248_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225827392))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226613888))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226614080)))];
tensor<fp16, [1, 77, 1024]> tensor_81_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("tensor_81_cast")];
tensor<int32, [4]> var_1253 = const()[name = tensor<string, []>("op_1253"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1254_cast = reshape(shape = var_1253, x = tensor_81_cast)[name = tensor<string, []>("op_1254_cast")];
tensor<int32, [4]> var_1255_perm_0 = const()[name = tensor<string, []>("op_1255_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1262 = const()[name = tensor<string, []>("op_1262"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1263_cast = reshape(shape = var_1262, x = tensor_83_cast)[name = tensor<string, []>("op_1263_cast")];
tensor<int32, [4]> var_1264_perm_0 = const()[name = tensor<string, []>("op_1264_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1266 = const()[name = tensor<string, []>("op_1266"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_49 = transpose(perm = var_1264_perm_0, x = var_1263_cast)[name = tensor<string, []>("transpose_49")];
tensor<fp16, [16, 77, 64]> query_states_27_cast = reshape(shape = var_1266, x = transpose_49)[name = tensor<string, []>("query_states_27_cast")];
tensor<int32, [3]> var_1268 = const()[name = tensor<string, []>("op_1268"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_48 = transpose(perm = var_1248_perm_0, x = var_1247_cast)[name = tensor<string, []>("transpose_48")];
tensor<fp16, [16, 77, 64]> key_states_55_cast = reshape(shape = var_1268, x = transpose_48)[name = tensor<string, []>("key_states_55_cast")];
tensor<int32, [3]> var_1270 = const()[name = tensor<string, []>("op_1270"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_47 = transpose(perm = var_1255_perm_0, x = var_1254_cast)[name = tensor<string, []>("transpose_47")];
tensor<fp16, [16, 77, 64]> value_states_55_cast = reshape(shape = var_1270, x = transpose_47)[name = tensor<string, []>("value_states_55_cast")];
tensor<int32, [3]> var_1273_perm_0 = const()[name = tensor<string, []>("op_1273_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_79_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_79_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_79_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_79_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_46 = transpose(perm = var_1273_perm_0, x = key_states_55_cast)[name = tensor<string, []>("transpose_46")];
tensor<fp16, [16, 77, 77]> attn_weights_79_cast = matmul(transpose_x = attn_weights_79_transpose_x_0, transpose_y = attn_weights_79_transpose_y_0, x = query_states_27_cast, y = transpose_46)[name = tensor<string, []>("attn_weights_79_cast")];
tensor<int32, [4]> var_1275 = const()[name = tensor<string, []>("op_1275"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1276_cast = reshape(shape = var_1275, x = attn_weights_79_cast)[name = tensor<string, []>("op_1276_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_81_cast = add(x = var_1276_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_81_cast")];
tensor<int32, [3]> var_1281 = const()[name = tensor<string, []>("op_1281"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_213_cast = reshape(shape = var_1281, x = attn_weights_81_cast)[name = tensor<string, []>("input_213_cast")];
tensor<fp16, [16, 77, 77]> input_215_cast = softmax(axis = var_5, x = input_213_cast)[name = tensor<string, []>("input_215_cast")];
tensor<bool, []> attn_output_79_transpose_x_0 = const()[name = tensor<string, []>("attn_output_79_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_79_transpose_y_0 = const()[name = tensor<string, []>("attn_output_79_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_79_cast = matmul(transpose_x = attn_output_79_transpose_x_0, transpose_y = attn_output_79_transpose_y_0, x = input_215_cast, y = value_states_55_cast)[name = tensor<string, []>("attn_output_79_cast")];
tensor<int32, [4]> var_1286 = const()[name = tensor<string, []>("op_1286"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_81_cast = reshape(shape = var_1286, x = attn_output_79_cast)[name = tensor<string, []>("attn_output_81_cast")];
tensor<int32, [4]> attn_output_83_perm_0 = const()[name = tensor<string, []>("attn_output_83_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1289 = const()[name = tensor<string, []>("op_1289"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_45 = transpose(perm = attn_output_83_perm_0, x = attn_output_81_cast)[name = tensor<string, []>("transpose_45")];
tensor<fp16, [1, 77, 1024]> input_217_cast = reshape(shape = var_1289, x = transpose_45)[name = tensor<string, []>("input_217_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226616192))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227402688))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227402880)))];
tensor<fp16, [1, 77, 1024]> hidden_states_81_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized, x = input_217_cast)[name = tensor<string, []>("hidden_states_81_cast")];
tensor<fp16, [1, 77, 1024]> input_219_cast = add(x = input_211_cast, y = hidden_states_81_cast)[name = tensor<string, []>("input_219_cast")];
tensor<int32, [1]> input_221_axes_0 = const()[name = tensor<string, []>("input_221_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227404992)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227407104)))];
tensor<fp16, [1, 77, 1024]> input_221_cast = layer_norm(axes = input_221_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16, x = input_219_cast)[name = tensor<string, []>("input_221_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227409216))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230555008))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230555200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230558336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_223_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized, x = input_221_cast)[name = tensor<string, []>("input_223_cast")];
tensor<string, []> input_225_mode_0 = const()[name = tensor<string, []>("input_225_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_225_cast = gelu(mode = input_225_mode_0, x = input_223_cast)[name = tensor<string, []>("input_225_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230558528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233704320))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233704512)))];
tensor<fp16, [1, 77, 1024]> hidden_states_83_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized, x = input_225_cast)[name = tensor<string, []>("hidden_states_83_cast")];
tensor<fp16, [1, 77, 1024]> input_227_cast = add(x = input_219_cast, y = hidden_states_83_cast)[name = tensor<string, []>("input_227_cast")];
tensor<int32, [1]> hidden_states_85_axes_0 = const()[name = tensor<string, []>("hidden_states_85_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233706624)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233708736)))];
tensor<fp16, [1, 77, 1024]> hidden_states_85_cast = layer_norm(axes = hidden_states_85_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16, x = input_227_cast)[name = tensor<string, []>("hidden_states_85_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233710848))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234497344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234497536)))];
tensor<fp16, [1, 77, 1024]> var_1327_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("op_1327_cast")];
tensor<fp16, []> var_1328_to_fp16 = const()[name = tensor<string, []>("op_1328_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_89_cast = mul(x = var_1327_cast, y = var_1328_to_fp16)[name = tensor<string, []>("tensor_89_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234499648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235286144))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235286336)))];
tensor<fp16, [1, 77, 1024]> tensor_85_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("tensor_85_cast")];
tensor<int32, [4]> var_1333 = const()[name = tensor<string, []>("op_1333"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1334_cast = reshape(shape = var_1333, x = tensor_85_cast)[name = tensor<string, []>("op_1334_cast")];
tensor<int32, [4]> var_1335_perm_0 = const()[name = tensor<string, []>("op_1335_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235288448))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236074944))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236075136)))];
tensor<fp16, [1, 77, 1024]> tensor_87_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("tensor_87_cast")];
tensor<int32, [4]> var_1340 = const()[name = tensor<string, []>("op_1340"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1341_cast = reshape(shape = var_1340, x = tensor_87_cast)[name = tensor<string, []>("op_1341_cast")];
tensor<int32, [4]> var_1342_perm_0 = const()[name = tensor<string, []>("op_1342_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1349 = const()[name = tensor<string, []>("op_1349"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1350_cast = reshape(shape = var_1349, x = tensor_89_cast)[name = tensor<string, []>("op_1350_cast")];
tensor<int32, [4]> var_1351_perm_0 = const()[name = tensor<string, []>("op_1351_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_44 = transpose(perm = var_1351_perm_0, x = var_1350_cast)[name = tensor<string, []>("transpose_44")];
tensor<fp16, [16, 77, 64]> query_states_29_cast = reshape(shape = var_1353, x = transpose_44)[name = tensor<string, []>("query_states_29_cast")];
tensor<int32, [3]> var_1355 = const()[name = tensor<string, []>("op_1355"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_43 = transpose(perm = var_1335_perm_0, x = var_1334_cast)[name = tensor<string, []>("transpose_43")];
tensor<fp16, [16, 77, 64]> key_states_59_cast = reshape(shape = var_1355, x = transpose_43)[name = tensor<string, []>("key_states_59_cast")];
tensor<int32, [3]> var_1357 = const()[name = tensor<string, []>("op_1357"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_42 = transpose(perm = var_1342_perm_0, x = var_1341_cast)[name = tensor<string, []>("transpose_42")];
tensor<fp16, [16, 77, 64]> value_states_59_cast = reshape(shape = var_1357, x = transpose_42)[name = tensor<string, []>("value_states_59_cast")];
tensor<int32, [3]> var_1360_perm_0 = const()[name = tensor<string, []>("op_1360_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_85_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_85_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_41 = transpose(perm = var_1360_perm_0, x = key_states_59_cast)[name = tensor<string, []>("transpose_41")];
tensor<fp16, [16, 77, 77]> attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = query_states_29_cast, y = transpose_41)[name = tensor<string, []>("attn_weights_85_cast")];
tensor<int32, [4]> var_1362 = const()[name = tensor<string, []>("op_1362"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1363_cast = reshape(shape = var_1362, x = attn_weights_85_cast)[name = tensor<string, []>("op_1363_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_87_cast = add(x = var_1363_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_87_cast")];
tensor<int32, [3]> var_1368 = const()[name = tensor<string, []>("op_1368"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_229_cast = reshape(shape = var_1368, x = attn_weights_87_cast)[name = tensor<string, []>("input_229_cast")];
tensor<fp16, [16, 77, 77]> input_231_cast = softmax(axis = var_5, x = input_229_cast)[name = tensor<string, []>("input_231_cast")];
tensor<bool, []> attn_output_85_transpose_x_0 = const()[name = tensor<string, []>("attn_output_85_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_85_transpose_y_0 = const()[name = tensor<string, []>("attn_output_85_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_85_cast = matmul(transpose_x = attn_output_85_transpose_x_0, transpose_y = attn_output_85_transpose_y_0, x = input_231_cast, y = value_states_59_cast)[name = tensor<string, []>("attn_output_85_cast")];
tensor<int32, [4]> var_1373 = const()[name = tensor<string, []>("op_1373"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_87_cast = reshape(shape = var_1373, x = attn_output_85_cast)[name = tensor<string, []>("attn_output_87_cast")];
tensor<int32, [4]> attn_output_89_perm_0 = const()[name = tensor<string, []>("attn_output_89_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1376 = const()[name = tensor<string, []>("op_1376"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_40 = transpose(perm = attn_output_89_perm_0, x = attn_output_87_cast)[name = tensor<string, []>("transpose_40")];
tensor<fp16, [1, 77, 1024]> input_233_cast = reshape(shape = var_1376, x = transpose_40)[name = tensor<string, []>("input_233_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236077248))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236863744))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236863936)))];
tensor<fp16, [1, 77, 1024]> hidden_states_87_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized, x = input_233_cast)[name = tensor<string, []>("hidden_states_87_cast")];
tensor<fp16, [1, 77, 1024]> input_235_cast = add(x = input_227_cast, y = hidden_states_87_cast)[name = tensor<string, []>("input_235_cast")];
tensor<int32, [1]> input_237_axes_0 = const()[name = tensor<string, []>("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236866048)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236868160)))];
tensor<fp16, [1, 77, 1024]> input_237_cast = layer_norm(axes = input_237_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16, x = input_235_cast)[name = tensor<string, []>("input_237_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236870272))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240016064))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240016256))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240019392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_239_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized, x = input_237_cast)[name = tensor<string, []>("input_239_cast")];
tensor<string, []> input_241_mode_0 = const()[name = tensor<string, []>("input_241_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_241_cast = gelu(mode = input_241_mode_0, x = input_239_cast)[name = tensor<string, []>("input_241_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240019584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243165376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243165568)))];
tensor<fp16, [1, 77, 1024]> hidden_states_89_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized, x = input_241_cast)[name = tensor<string, []>("hidden_states_89_cast")];
tensor<fp16, [1, 77, 1024]> input_243_cast = add(x = input_235_cast, y = hidden_states_89_cast)[name = tensor<string, []>("input_243_cast")];
tensor<int32, [1]> hidden_states_91_axes_0 = const()[name = tensor<string, []>("hidden_states_91_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243167680)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243169792)))];
tensor<fp16, [1, 77, 1024]> hidden_states_91_cast = layer_norm(axes = hidden_states_91_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16, x = input_243_cast)[name = tensor<string, []>("hidden_states_91_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243171904))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243958400))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243958592)))];
tensor<fp16, [1, 77, 1024]> var_1414_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("op_1414_cast")];
tensor<fp16, []> var_1415_to_fp16 = const()[name = tensor<string, []>("op_1415_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_95_cast = mul(x = var_1414_cast, y = var_1415_to_fp16)[name = tensor<string, []>("tensor_95_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243960704))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244747200))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244747392)))];
tensor<fp16, [1, 77, 1024]> tensor_91_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("tensor_91_cast")];
tensor<int32, [4]> var_1420 = const()[name = tensor<string, []>("op_1420"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1421_cast = reshape(shape = var_1420, x = tensor_91_cast)[name = tensor<string, []>("op_1421_cast")];
tensor<int32, [4]> var_1422_perm_0 = const()[name = tensor<string, []>("op_1422_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244749504))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(245536000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(245536192)))];
tensor<fp16, [1, 77, 1024]> tensor_93_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("tensor_93_cast")];
tensor<int32, [4]> var_1427 = const()[name = tensor<string, []>("op_1427"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1428_cast = reshape(shape = var_1427, x = tensor_93_cast)[name = tensor<string, []>("op_1428_cast")];
tensor<int32, [4]> var_1429_perm_0 = const()[name = tensor<string, []>("op_1429_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1436 = const()[name = tensor<string, []>("op_1436"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1437_cast = reshape(shape = var_1436, x = tensor_95_cast)[name = tensor<string, []>("op_1437_cast")];
tensor<int32, [4]> var_1438_perm_0 = const()[name = tensor<string, []>("op_1438_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1440 = const()[name = tensor<string, []>("op_1440"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_39 = transpose(perm = var_1438_perm_0, x = var_1437_cast)[name = tensor<string, []>("transpose_39")];
tensor<fp16, [16, 77, 64]> query_states_31_cast = reshape(shape = var_1440, x = transpose_39)[name = tensor<string, []>("query_states_31_cast")];
tensor<int32, [3]> var_1442 = const()[name = tensor<string, []>("op_1442"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_38 = transpose(perm = var_1422_perm_0, x = var_1421_cast)[name = tensor<string, []>("transpose_38")];
tensor<fp16, [16, 77, 64]> key_states_63_cast = reshape(shape = var_1442, x = transpose_38)[name = tensor<string, []>("key_states_63_cast")];
tensor<int32, [3]> var_1444 = const()[name = tensor<string, []>("op_1444"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_37 = transpose(perm = var_1429_perm_0, x = var_1428_cast)[name = tensor<string, []>("transpose_37")];
tensor<fp16, [16, 77, 64]> value_states_63_cast = reshape(shape = var_1444, x = transpose_37)[name = tensor<string, []>("value_states_63_cast")];
tensor<int32, [3]> var_1447_perm_0 = const()[name = tensor<string, []>("op_1447_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_91_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_91_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_91_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_91_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_36 = transpose(perm = var_1447_perm_0, x = key_states_63_cast)[name = tensor<string, []>("transpose_36")];
tensor<fp16, [16, 77, 77]> attn_weights_91_cast = matmul(transpose_x = attn_weights_91_transpose_x_0, transpose_y = attn_weights_91_transpose_y_0, x = query_states_31_cast, y = transpose_36)[name = tensor<string, []>("attn_weights_91_cast")];
tensor<int32, [4]> var_1449 = const()[name = tensor<string, []>("op_1449"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1450_cast = reshape(shape = var_1449, x = attn_weights_91_cast)[name = tensor<string, []>("op_1450_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_93_cast = add(x = var_1450_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_93_cast")];
tensor<int32, [3]> var_1455 = const()[name = tensor<string, []>("op_1455"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_245_cast = reshape(shape = var_1455, x = attn_weights_93_cast)[name = tensor<string, []>("input_245_cast")];
tensor<fp16, [16, 77, 77]> input_247_cast = softmax(axis = var_5, x = input_245_cast)[name = tensor<string, []>("input_247_cast")];
tensor<bool, []> attn_output_91_transpose_x_0 = const()[name = tensor<string, []>("attn_output_91_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_91_transpose_y_0 = const()[name = tensor<string, []>("attn_output_91_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_91_cast = matmul(transpose_x = attn_output_91_transpose_x_0, transpose_y = attn_output_91_transpose_y_0, x = input_247_cast, y = value_states_63_cast)[name = tensor<string, []>("attn_output_91_cast")];
tensor<int32, [4]> var_1460 = const()[name = tensor<string, []>("op_1460"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_93_cast = reshape(shape = var_1460, x = attn_output_91_cast)[name = tensor<string, []>("attn_output_93_cast")];
tensor<int32, [4]> attn_output_95_perm_0 = const()[name = tensor<string, []>("attn_output_95_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1463 = const()[name = tensor<string, []>("op_1463"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_35 = transpose(perm = attn_output_95_perm_0, x = attn_output_93_cast)[name = tensor<string, []>("transpose_35")];
tensor<fp16, [1, 77, 1024]> input_249_cast = reshape(shape = var_1463, x = transpose_35)[name = tensor<string, []>("input_249_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(245538304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246324800))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246324992)))];
tensor<fp16, [1, 77, 1024]> hidden_states_93_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized, x = input_249_cast)[name = tensor<string, []>("hidden_states_93_cast")];
tensor<fp16, [1, 77, 1024]> input_251_cast = add(x = input_243_cast, y = hidden_states_93_cast)[name = tensor<string, []>("input_251_cast")];
tensor<int32, [1]> input_253_axes_0 = const()[name = tensor<string, []>("input_253_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246327104)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246329216)))];
tensor<fp16, [1, 77, 1024]> input_253_cast = layer_norm(axes = input_253_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16, x = input_251_cast)[name = tensor<string, []>("input_253_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246331328))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249477120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249477312))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249480448))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_255_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized, x = input_253_cast)[name = tensor<string, []>("input_255_cast")];
tensor<string, []> input_257_mode_0 = const()[name = tensor<string, []>("input_257_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_257_cast = gelu(mode = input_257_mode_0, x = input_255_cast)[name = tensor<string, []>("input_257_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249480640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252626432))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252626624)))];
tensor<fp16, [1, 77, 1024]> hidden_states_95_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized, x = input_257_cast)[name = tensor<string, []>("hidden_states_95_cast")];
tensor<fp16, [1, 77, 1024]> input_259_cast = add(x = input_251_cast, y = hidden_states_95_cast)[name = tensor<string, []>("input_259_cast")];
tensor<int32, [1]> hidden_states_97_axes_0 = const()[name = tensor<string, []>("hidden_states_97_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252628736)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252630848)))];
tensor<fp16, [1, 77, 1024]> hidden_states_97_cast = layer_norm(axes = hidden_states_97_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16, x = input_259_cast)[name = tensor<string, []>("hidden_states_97_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252632960))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253419456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253419648)))];
tensor<fp16, [1, 77, 1024]> var_1501_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("op_1501_cast")];
tensor<fp16, []> var_1502_to_fp16 = const()[name = tensor<string, []>("op_1502_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_101_cast = mul(x = var_1501_cast, y = var_1502_to_fp16)[name = tensor<string, []>("tensor_101_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253421760))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254208256))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254208448)))];
tensor<fp16, [1, 77, 1024]> tensor_97_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("tensor_97_cast")];
tensor<int32, [4]> var_1507 = const()[name = tensor<string, []>("op_1507"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1508_cast = reshape(shape = var_1507, x = tensor_97_cast)[name = tensor<string, []>("op_1508_cast")];
tensor<int32, [4]> var_1509_perm_0 = const()[name = tensor<string, []>("op_1509_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254210560))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254997056))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254997248)))];
tensor<fp16, [1, 77, 1024]> tensor_99_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("tensor_99_cast")];
tensor<int32, [4]> var_1514 = const()[name = tensor<string, []>("op_1514"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1515_cast = reshape(shape = var_1514, x = tensor_99_cast)[name = tensor<string, []>("op_1515_cast")];
tensor<int32, [4]> var_1516_perm_0 = const()[name = tensor<string, []>("op_1516_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1523 = const()[name = tensor<string, []>("op_1523"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1524_cast = reshape(shape = var_1523, x = tensor_101_cast)[name = tensor<string, []>("op_1524_cast")];
tensor<int32, [4]> var_1525_perm_0 = const()[name = tensor<string, []>("op_1525_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1527 = const()[name = tensor<string, []>("op_1527"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_34 = transpose(perm = var_1525_perm_0, x = var_1524_cast)[name = tensor<string, []>("transpose_34")];
tensor<fp16, [16, 77, 64]> query_states_33_cast = reshape(shape = var_1527, x = transpose_34)[name = tensor<string, []>("query_states_33_cast")];
tensor<int32, [3]> var_1529 = const()[name = tensor<string, []>("op_1529"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_33 = transpose(perm = var_1509_perm_0, x = var_1508_cast)[name = tensor<string, []>("transpose_33")];
tensor<fp16, [16, 77, 64]> key_states_67_cast = reshape(shape = var_1529, x = transpose_33)[name = tensor<string, []>("key_states_67_cast")];
tensor<int32, [3]> var_1531 = const()[name = tensor<string, []>("op_1531"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_32 = transpose(perm = var_1516_perm_0, x = var_1515_cast)[name = tensor<string, []>("transpose_32")];
tensor<fp16, [16, 77, 64]> value_states_67_cast = reshape(shape = var_1531, x = transpose_32)[name = tensor<string, []>("value_states_67_cast")];
tensor<int32, [3]> var_1534_perm_0 = const()[name = tensor<string, []>("op_1534_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_97_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_97_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_31 = transpose(perm = var_1534_perm_0, x = key_states_67_cast)[name = tensor<string, []>("transpose_31")];
tensor<fp16, [16, 77, 77]> attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = query_states_33_cast, y = transpose_31)[name = tensor<string, []>("attn_weights_97_cast")];
tensor<int32, [4]> var_1536 = const()[name = tensor<string, []>("op_1536"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1537_cast = reshape(shape = var_1536, x = attn_weights_97_cast)[name = tensor<string, []>("op_1537_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_99_cast = add(x = var_1537_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_99_cast")];
tensor<int32, [3]> var_1542 = const()[name = tensor<string, []>("op_1542"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_261_cast = reshape(shape = var_1542, x = attn_weights_99_cast)[name = tensor<string, []>("input_261_cast")];
tensor<fp16, [16, 77, 77]> input_263_cast = softmax(axis = var_5, x = input_261_cast)[name = tensor<string, []>("input_263_cast")];
tensor<bool, []> attn_output_97_transpose_x_0 = const()[name = tensor<string, []>("attn_output_97_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_97_transpose_y_0 = const()[name = tensor<string, []>("attn_output_97_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_97_cast = matmul(transpose_x = attn_output_97_transpose_x_0, transpose_y = attn_output_97_transpose_y_0, x = input_263_cast, y = value_states_67_cast)[name = tensor<string, []>("attn_output_97_cast")];
tensor<int32, [4]> var_1547 = const()[name = tensor<string, []>("op_1547"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_99_cast = reshape(shape = var_1547, x = attn_output_97_cast)[name = tensor<string, []>("attn_output_99_cast")];
tensor<int32, [4]> attn_output_101_perm_0 = const()[name = tensor<string, []>("attn_output_101_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1550 = const()[name = tensor<string, []>("op_1550"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_30 = transpose(perm = attn_output_101_perm_0, x = attn_output_99_cast)[name = tensor<string, []>("transpose_30")];
tensor<fp16, [1, 77, 1024]> input_265_cast = reshape(shape = var_1550, x = transpose_30)[name = tensor<string, []>("input_265_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254999360))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255785856))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255786048)))];
tensor<fp16, [1, 77, 1024]> hidden_states_99_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized, x = input_265_cast)[name = tensor<string, []>("hidden_states_99_cast")];
tensor<fp16, [1, 77, 1024]> input_267_cast = add(x = input_259_cast, y = hidden_states_99_cast)[name = tensor<string, []>("input_267_cast")];
tensor<int32, [1]> input_269_axes_0 = const()[name = tensor<string, []>("input_269_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255788160)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255790272)))];
tensor<fp16, [1, 77, 1024]> input_269_cast = layer_norm(axes = input_269_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16, x = input_267_cast)[name = tensor<string, []>("input_269_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255792384))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258938176))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258938368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258941504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_271_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized, x = input_269_cast)[name = tensor<string, []>("input_271_cast")];
tensor<string, []> input_273_mode_0 = const()[name = tensor<string, []>("input_273_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_273_cast = gelu(mode = input_273_mode_0, x = input_271_cast)[name = tensor<string, []>("input_273_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258941696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262087488))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262087680)))];
tensor<fp16, [1, 77, 1024]> hidden_states_101_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized, x = input_273_cast)[name = tensor<string, []>("hidden_states_101_cast")];
tensor<fp16, [1, 77, 1024]> input_275_cast = add(x = input_267_cast, y = hidden_states_101_cast)[name = tensor<string, []>("input_275_cast")];
tensor<int32, [1]> hidden_states_103_axes_0 = const()[name = tensor<string, []>("hidden_states_103_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262089792)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262091904)))];
tensor<fp16, [1, 77, 1024]> hidden_states_103_cast = layer_norm(axes = hidden_states_103_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16, x = input_275_cast)[name = tensor<string, []>("hidden_states_103_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262094016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262880512))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262880704)))];
tensor<fp16, [1, 77, 1024]> var_1588_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("op_1588_cast")];
tensor<fp16, []> var_1589_to_fp16 = const()[name = tensor<string, []>("op_1589_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_107_cast = mul(x = var_1588_cast, y = var_1589_to_fp16)[name = tensor<string, []>("tensor_107_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262882816))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263669312))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263669504)))];
tensor<fp16, [1, 77, 1024]> tensor_103_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("tensor_103_cast")];
tensor<int32, [4]> var_1594 = const()[name = tensor<string, []>("op_1594"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1595_cast = reshape(shape = var_1594, x = tensor_103_cast)[name = tensor<string, []>("op_1595_cast")];
tensor<int32, [4]> var_1596_perm_0 = const()[name = tensor<string, []>("op_1596_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263671616))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264458112))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264458304)))];
tensor<fp16, [1, 77, 1024]> tensor_105_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("tensor_105_cast")];
tensor<int32, [4]> var_1601 = const()[name = tensor<string, []>("op_1601"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1602_cast = reshape(shape = var_1601, x = tensor_105_cast)[name = tensor<string, []>("op_1602_cast")];
tensor<int32, [4]> var_1603_perm_0 = const()[name = tensor<string, []>("op_1603_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1610 = const()[name = tensor<string, []>("op_1610"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1611_cast = reshape(shape = var_1610, x = tensor_107_cast)[name = tensor<string, []>("op_1611_cast")];
tensor<int32, [4]> var_1612_perm_0 = const()[name = tensor<string, []>("op_1612_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1614 = const()[name = tensor<string, []>("op_1614"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_29 = transpose(perm = var_1612_perm_0, x = var_1611_cast)[name = tensor<string, []>("transpose_29")];
tensor<fp16, [16, 77, 64]> query_states_35_cast = reshape(shape = var_1614, x = transpose_29)[name = tensor<string, []>("query_states_35_cast")];
tensor<int32, [3]> var_1616 = const()[name = tensor<string, []>("op_1616"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_28 = transpose(perm = var_1596_perm_0, x = var_1595_cast)[name = tensor<string, []>("transpose_28")];
tensor<fp16, [16, 77, 64]> key_states_71_cast = reshape(shape = var_1616, x = transpose_28)[name = tensor<string, []>("key_states_71_cast")];
tensor<int32, [3]> var_1618 = const()[name = tensor<string, []>("op_1618"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_27 = transpose(perm = var_1603_perm_0, x = var_1602_cast)[name = tensor<string, []>("transpose_27")];
tensor<fp16, [16, 77, 64]> value_states_71_cast = reshape(shape = var_1618, x = transpose_27)[name = tensor<string, []>("value_states_71_cast")];
tensor<int32, [3]> var_1621_perm_0 = const()[name = tensor<string, []>("op_1621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_103_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_103_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_103_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_103_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_26 = transpose(perm = var_1621_perm_0, x = key_states_71_cast)[name = tensor<string, []>("transpose_26")];
tensor<fp16, [16, 77, 77]> attn_weights_103_cast = matmul(transpose_x = attn_weights_103_transpose_x_0, transpose_y = attn_weights_103_transpose_y_0, x = query_states_35_cast, y = transpose_26)[name = tensor<string, []>("attn_weights_103_cast")];
tensor<int32, [4]> var_1623 = const()[name = tensor<string, []>("op_1623"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1624_cast = reshape(shape = var_1623, x = attn_weights_103_cast)[name = tensor<string, []>("op_1624_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_105_cast = add(x = var_1624_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_105_cast")];
tensor<int32, [3]> var_1629 = const()[name = tensor<string, []>("op_1629"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_277_cast = reshape(shape = var_1629, x = attn_weights_105_cast)[name = tensor<string, []>("input_277_cast")];
tensor<fp16, [16, 77, 77]> input_279_cast = softmax(axis = var_5, x = input_277_cast)[name = tensor<string, []>("input_279_cast")];
tensor<bool, []> attn_output_103_transpose_x_0 = const()[name = tensor<string, []>("attn_output_103_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_103_transpose_y_0 = const()[name = tensor<string, []>("attn_output_103_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_103_cast = matmul(transpose_x = attn_output_103_transpose_x_0, transpose_y = attn_output_103_transpose_y_0, x = input_279_cast, y = value_states_71_cast)[name = tensor<string, []>("attn_output_103_cast")];
tensor<int32, [4]> var_1634 = const()[name = tensor<string, []>("op_1634"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_105_cast = reshape(shape = var_1634, x = attn_output_103_cast)[name = tensor<string, []>("attn_output_105_cast")];
tensor<int32, [4]> attn_output_107_perm_0 = const()[name = tensor<string, []>("attn_output_107_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1637 = const()[name = tensor<string, []>("op_1637"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_25 = transpose(perm = attn_output_107_perm_0, x = attn_output_105_cast)[name = tensor<string, []>("transpose_25")];
tensor<fp16, [1, 77, 1024]> input_281_cast = reshape(shape = var_1637, x = transpose_25)[name = tensor<string, []>("input_281_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264460416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265246912))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265247104)))];
tensor<fp16, [1, 77, 1024]> hidden_states_105_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized, x = input_281_cast)[name = tensor<string, []>("hidden_states_105_cast")];
tensor<fp16, [1, 77, 1024]> input_283_cast = add(x = input_275_cast, y = hidden_states_105_cast)[name = tensor<string, []>("input_283_cast")];
tensor<int32, [1]> input_285_axes_0 = const()[name = tensor<string, []>("input_285_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265249216)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265251328)))];
tensor<fp16, [1, 77, 1024]> input_285_cast = layer_norm(axes = input_285_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16, x = input_283_cast)[name = tensor<string, []>("input_285_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265253440))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268399232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268399424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268402560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_287_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized, x = input_285_cast)[name = tensor<string, []>("input_287_cast")];
tensor<string, []> input_289_mode_0 = const()[name = tensor<string, []>("input_289_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_289_cast = gelu(mode = input_289_mode_0, x = input_287_cast)[name = tensor<string, []>("input_289_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268402752))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271548544))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271548736)))];
tensor<fp16, [1, 77, 1024]> hidden_states_107_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized, x = input_289_cast)[name = tensor<string, []>("hidden_states_107_cast")];
tensor<fp16, [1, 77, 1024]> input_291_cast = add(x = input_283_cast, y = hidden_states_107_cast)[name = tensor<string, []>("input_291_cast")];
tensor<int32, [1]> hidden_states_109_axes_0 = const()[name = tensor<string, []>("hidden_states_109_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271550848)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271552960)))];
tensor<fp16, [1, 77, 1024]> hidden_states_109_cast = layer_norm(axes = hidden_states_109_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16, x = input_291_cast)[name = tensor<string, []>("hidden_states_109_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271555072))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(272341568))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(272341760)))];
tensor<fp16, [1, 77, 1024]> var_1675_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("op_1675_cast")];
tensor<fp16, []> var_1676_to_fp16 = const()[name = tensor<string, []>("op_1676_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_113_cast = mul(x = var_1675_cast, y = var_1676_to_fp16)[name = tensor<string, []>("tensor_113_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(272343872))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273130368))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273130560)))];
tensor<fp16, [1, 77, 1024]> tensor_109_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("tensor_109_cast")];
tensor<int32, [4]> var_1681 = const()[name = tensor<string, []>("op_1681"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1682_cast = reshape(shape = var_1681, x = tensor_109_cast)[name = tensor<string, []>("op_1682_cast")];
tensor<int32, [4]> var_1683_perm_0 = const()[name = tensor<string, []>("op_1683_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273132672))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273919168))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273919360)))];
tensor<fp16, [1, 77, 1024]> tensor_111_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("tensor_111_cast")];
tensor<int32, [4]> var_1688 = const()[name = tensor<string, []>("op_1688"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1689_cast = reshape(shape = var_1688, x = tensor_111_cast)[name = tensor<string, []>("op_1689_cast")];
tensor<int32, [4]> var_1690_perm_0 = const()[name = tensor<string, []>("op_1690_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1697 = const()[name = tensor<string, []>("op_1697"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1698_cast = reshape(shape = var_1697, x = tensor_113_cast)[name = tensor<string, []>("op_1698_cast")];
tensor<int32, [4]> var_1699_perm_0 = const()[name = tensor<string, []>("op_1699_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1701 = const()[name = tensor<string, []>("op_1701"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_24 = transpose(perm = var_1699_perm_0, x = var_1698_cast)[name = tensor<string, []>("transpose_24")];
tensor<fp16, [16, 77, 64]> query_states_37_cast = reshape(shape = var_1701, x = transpose_24)[name = tensor<string, []>("query_states_37_cast")];
tensor<int32, [3]> var_1703 = const()[name = tensor<string, []>("op_1703"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_23 = transpose(perm = var_1683_perm_0, x = var_1682_cast)[name = tensor<string, []>("transpose_23")];
tensor<fp16, [16, 77, 64]> key_states_75_cast = reshape(shape = var_1703, x = transpose_23)[name = tensor<string, []>("key_states_75_cast")];
tensor<int32, [3]> var_1705 = const()[name = tensor<string, []>("op_1705"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_22 = transpose(perm = var_1690_perm_0, x = var_1689_cast)[name = tensor<string, []>("transpose_22")];
tensor<fp16, [16, 77, 64]> value_states_75_cast = reshape(shape = var_1705, x = transpose_22)[name = tensor<string, []>("value_states_75_cast")];
tensor<int32, [3]> var_1708_perm_0 = const()[name = tensor<string, []>("op_1708_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_109_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_109_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_21 = transpose(perm = var_1708_perm_0, x = key_states_75_cast)[name = tensor<string, []>("transpose_21")];
tensor<fp16, [16, 77, 77]> attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = query_states_37_cast, y = transpose_21)[name = tensor<string, []>("attn_weights_109_cast")];
tensor<int32, [4]> var_1710 = const()[name = tensor<string, []>("op_1710"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1711_cast = reshape(shape = var_1710, x = attn_weights_109_cast)[name = tensor<string, []>("op_1711_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_111_cast = add(x = var_1711_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_111_cast")];
tensor<int32, [3]> var_1716 = const()[name = tensor<string, []>("op_1716"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_293_cast = reshape(shape = var_1716, x = attn_weights_111_cast)[name = tensor<string, []>("input_293_cast")];
tensor<fp16, [16, 77, 77]> input_295_cast = softmax(axis = var_5, x = input_293_cast)[name = tensor<string, []>("input_295_cast")];
tensor<bool, []> attn_output_109_transpose_x_0 = const()[name = tensor<string, []>("attn_output_109_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_109_transpose_y_0 = const()[name = tensor<string, []>("attn_output_109_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_109_cast = matmul(transpose_x = attn_output_109_transpose_x_0, transpose_y = attn_output_109_transpose_y_0, x = input_295_cast, y = value_states_75_cast)[name = tensor<string, []>("attn_output_109_cast")];
tensor<int32, [4]> var_1721 = const()[name = tensor<string, []>("op_1721"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_111_cast = reshape(shape = var_1721, x = attn_output_109_cast)[name = tensor<string, []>("attn_output_111_cast")];
tensor<int32, [4]> attn_output_113_perm_0 = const()[name = tensor<string, []>("attn_output_113_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1724 = const()[name = tensor<string, []>("op_1724"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_20 = transpose(perm = attn_output_113_perm_0, x = attn_output_111_cast)[name = tensor<string, []>("transpose_20")];
tensor<fp16, [1, 77, 1024]> input_297_cast = reshape(shape = var_1724, x = transpose_20)[name = tensor<string, []>("input_297_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273921472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274707968))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274708160)))];
tensor<fp16, [1, 77, 1024]> hidden_states_111_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized, x = input_297_cast)[name = tensor<string, []>("hidden_states_111_cast")];
tensor<fp16, [1, 77, 1024]> input_299_cast = add(x = input_291_cast, y = hidden_states_111_cast)[name = tensor<string, []>("input_299_cast")];
tensor<int32, [1]> input_301_axes_0 = const()[name = tensor<string, []>("input_301_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274710272)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274712384)))];
tensor<fp16, [1, 77, 1024]> input_301_cast = layer_norm(axes = input_301_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16, x = input_299_cast)[name = tensor<string, []>("input_301_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274714496))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277860288))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277860480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277863616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_303_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized, x = input_301_cast)[name = tensor<string, []>("input_303_cast")];
tensor<string, []> input_305_mode_0 = const()[name = tensor<string, []>("input_305_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_305_cast = gelu(mode = input_305_mode_0, x = input_303_cast)[name = tensor<string, []>("input_305_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277863808))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281009600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281009792)))];
tensor<fp16, [1, 77, 1024]> hidden_states_113_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized, x = input_305_cast)[name = tensor<string, []>("hidden_states_113_cast")];
tensor<fp16, [1, 77, 1024]> input_307_cast = add(x = input_299_cast, y = hidden_states_113_cast)[name = tensor<string, []>("input_307_cast")];
tensor<int32, [1]> hidden_states_115_axes_0 = const()[name = tensor<string, []>("hidden_states_115_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281011904)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281014016)))];
tensor<fp16, [1, 77, 1024]> hidden_states_115_cast = layer_norm(axes = hidden_states_115_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16, x = input_307_cast)[name = tensor<string, []>("hidden_states_115_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281016128))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281802624))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281802816)))];
tensor<fp16, [1, 77, 1024]> var_1762_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("op_1762_cast")];
tensor<fp16, []> var_1763_to_fp16 = const()[name = tensor<string, []>("op_1763_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_119_cast = mul(x = var_1762_cast, y = var_1763_to_fp16)[name = tensor<string, []>("tensor_119_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281804928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282591424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282591616)))];
tensor<fp16, [1, 77, 1024]> tensor_115_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("tensor_115_cast")];
tensor<int32, [4]> var_1768 = const()[name = tensor<string, []>("op_1768"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1769_cast = reshape(shape = var_1768, x = tensor_115_cast)[name = tensor<string, []>("op_1769_cast")];
tensor<int32, [4]> var_1770_perm_0 = const()[name = tensor<string, []>("op_1770_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282593728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283380224))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283380416)))];
tensor<fp16, [1, 77, 1024]> tensor_117_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("tensor_117_cast")];
tensor<int32, [4]> var_1775 = const()[name = tensor<string, []>("op_1775"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1776_cast = reshape(shape = var_1775, x = tensor_117_cast)[name = tensor<string, []>("op_1776_cast")];
tensor<int32, [4]> var_1777_perm_0 = const()[name = tensor<string, []>("op_1777_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1784 = const()[name = tensor<string, []>("op_1784"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1785_cast = reshape(shape = var_1784, x = tensor_119_cast)[name = tensor<string, []>("op_1785_cast")];
tensor<int32, [4]> var_1786_perm_0 = const()[name = tensor<string, []>("op_1786_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1788 = const()[name = tensor<string, []>("op_1788"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_19 = transpose(perm = var_1786_perm_0, x = var_1785_cast)[name = tensor<string, []>("transpose_19")];
tensor<fp16, [16, 77, 64]> query_states_39_cast = reshape(shape = var_1788, x = transpose_19)[name = tensor<string, []>("query_states_39_cast")];
tensor<int32, [3]> var_1790 = const()[name = tensor<string, []>("op_1790"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_18 = transpose(perm = var_1770_perm_0, x = var_1769_cast)[name = tensor<string, []>("transpose_18")];
tensor<fp16, [16, 77, 64]> key_states_79_cast = reshape(shape = var_1790, x = transpose_18)[name = tensor<string, []>("key_states_79_cast")];
tensor<int32, [3]> var_1792 = const()[name = tensor<string, []>("op_1792"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_17 = transpose(perm = var_1777_perm_0, x = var_1776_cast)[name = tensor<string, []>("transpose_17")];
tensor<fp16, [16, 77, 64]> value_states_79_cast = reshape(shape = var_1792, x = transpose_17)[name = tensor<string, []>("value_states_79_cast")];
tensor<int32, [3]> var_1795_perm_0 = const()[name = tensor<string, []>("op_1795_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_115_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_115_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_115_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_115_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_16 = transpose(perm = var_1795_perm_0, x = key_states_79_cast)[name = tensor<string, []>("transpose_16")];
tensor<fp16, [16, 77, 77]> attn_weights_115_cast = matmul(transpose_x = attn_weights_115_transpose_x_0, transpose_y = attn_weights_115_transpose_y_0, x = query_states_39_cast, y = transpose_16)[name = tensor<string, []>("attn_weights_115_cast")];
tensor<int32, [4]> var_1797 = const()[name = tensor<string, []>("op_1797"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1798_cast = reshape(shape = var_1797, x = attn_weights_115_cast)[name = tensor<string, []>("op_1798_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_117_cast = add(x = var_1798_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_117_cast")];
tensor<int32, [3]> var_1803 = const()[name = tensor<string, []>("op_1803"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_309_cast = reshape(shape = var_1803, x = attn_weights_117_cast)[name = tensor<string, []>("input_309_cast")];
tensor<fp16, [16, 77, 77]> input_311_cast = softmax(axis = var_5, x = input_309_cast)[name = tensor<string, []>("input_311_cast")];
tensor<bool, []> attn_output_115_transpose_x_0 = const()[name = tensor<string, []>("attn_output_115_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_115_transpose_y_0 = const()[name = tensor<string, []>("attn_output_115_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_115_cast = matmul(transpose_x = attn_output_115_transpose_x_0, transpose_y = attn_output_115_transpose_y_0, x = input_311_cast, y = value_states_79_cast)[name = tensor<string, []>("attn_output_115_cast")];
tensor<int32, [4]> var_1808 = const()[name = tensor<string, []>("op_1808"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_117_cast = reshape(shape = var_1808, x = attn_output_115_cast)[name = tensor<string, []>("attn_output_117_cast")];
tensor<int32, [4]> attn_output_119_perm_0 = const()[name = tensor<string, []>("attn_output_119_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1811 = const()[name = tensor<string, []>("op_1811"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_15 = transpose(perm = attn_output_119_perm_0, x = attn_output_117_cast)[name = tensor<string, []>("transpose_15")];
tensor<fp16, [1, 77, 1024]> input_313_cast = reshape(shape = var_1811, x = transpose_15)[name = tensor<string, []>("input_313_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283382528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284169024))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284169216)))];
tensor<fp16, [1, 77, 1024]> hidden_states_117_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized, x = input_313_cast)[name = tensor<string, []>("hidden_states_117_cast")];
tensor<fp16, [1, 77, 1024]> input_315_cast = add(x = input_307_cast, y = hidden_states_117_cast)[name = tensor<string, []>("input_315_cast")];
tensor<int32, [1]> input_317_axes_0 = const()[name = tensor<string, []>("input_317_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284171328)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284173440)))];
tensor<fp16, [1, 77, 1024]> input_317_cast = layer_norm(axes = input_317_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16, x = input_315_cast)[name = tensor<string, []>("input_317_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284175552))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287321344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287321536))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287324672))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_319_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized, x = input_317_cast)[name = tensor<string, []>("input_319_cast")];
tensor<string, []> input_321_mode_0 = const()[name = tensor<string, []>("input_321_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_321_cast = gelu(mode = input_321_mode_0, x = input_319_cast)[name = tensor<string, []>("input_321_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287324864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290470656))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290470848)))];
tensor<fp16, [1, 77, 1024]> hidden_states_119_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized, x = input_321_cast)[name = tensor<string, []>("hidden_states_119_cast")];
tensor<fp16, [1, 77, 1024]> input_323_cast = add(x = input_315_cast, y = hidden_states_119_cast)[name = tensor<string, []>("input_323_cast")];
tensor<int32, [1]> hidden_states_121_axes_0 = const()[name = tensor<string, []>("hidden_states_121_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290472960)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290475072)))];
tensor<fp16, [1, 77, 1024]> hidden_states_121_cast = layer_norm(axes = hidden_states_121_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16, x = input_323_cast)[name = tensor<string, []>("hidden_states_121_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290477184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291263680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291263872)))];
tensor<fp16, [1, 77, 1024]> var_1849_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("op_1849_cast")];
tensor<fp16, []> var_1850_to_fp16 = const()[name = tensor<string, []>("op_1850_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_125_cast = mul(x = var_1849_cast, y = var_1850_to_fp16)[name = tensor<string, []>("tensor_125_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291265984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292052480))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292052672)))];
tensor<fp16, [1, 77, 1024]> tensor_121_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("tensor_121_cast")];
tensor<int32, [4]> var_1855 = const()[name = tensor<string, []>("op_1855"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1856_cast = reshape(shape = var_1855, x = tensor_121_cast)[name = tensor<string, []>("op_1856_cast")];
tensor<int32, [4]> var_1857_perm_0 = const()[name = tensor<string, []>("op_1857_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292054784))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292841280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292841472)))];
tensor<fp16, [1, 77, 1024]> tensor_123_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("tensor_123_cast")];
tensor<int32, [4]> var_1862 = const()[name = tensor<string, []>("op_1862"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1863_cast = reshape(shape = var_1862, x = tensor_123_cast)[name = tensor<string, []>("op_1863_cast")];
tensor<int32, [4]> var_1864_perm_0 = const()[name = tensor<string, []>("op_1864_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1871 = const()[name = tensor<string, []>("op_1871"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1872_cast = reshape(shape = var_1871, x = tensor_125_cast)[name = tensor<string, []>("op_1872_cast")];
tensor<int32, [4]> var_1873_perm_0 = const()[name = tensor<string, []>("op_1873_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1875 = const()[name = tensor<string, []>("op_1875"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_14 = transpose(perm = var_1873_perm_0, x = var_1872_cast)[name = tensor<string, []>("transpose_14")];
tensor<fp16, [16, 77, 64]> query_states_41_cast = reshape(shape = var_1875, x = transpose_14)[name = tensor<string, []>("query_states_41_cast")];
tensor<int32, [3]> var_1877 = const()[name = tensor<string, []>("op_1877"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_13 = transpose(perm = var_1857_perm_0, x = var_1856_cast)[name = tensor<string, []>("transpose_13")];
tensor<fp16, [16, 77, 64]> key_states_83_cast = reshape(shape = var_1877, x = transpose_13)[name = tensor<string, []>("key_states_83_cast")];
tensor<int32, [3]> var_1879 = const()[name = tensor<string, []>("op_1879"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_12 = transpose(perm = var_1864_perm_0, x = var_1863_cast)[name = tensor<string, []>("transpose_12")];
tensor<fp16, [16, 77, 64]> value_states_83_cast = reshape(shape = var_1879, x = transpose_12)[name = tensor<string, []>("value_states_83_cast")];
tensor<int32, [3]> var_1882_perm_0 = const()[name = tensor<string, []>("op_1882_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_121_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_121_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_11 = transpose(perm = var_1882_perm_0, x = key_states_83_cast)[name = tensor<string, []>("transpose_11")];
tensor<fp16, [16, 77, 77]> attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = query_states_41_cast, y = transpose_11)[name = tensor<string, []>("attn_weights_121_cast")];
tensor<int32, [4]> var_1884 = const()[name = tensor<string, []>("op_1884"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1885_cast = reshape(shape = var_1884, x = attn_weights_121_cast)[name = tensor<string, []>("op_1885_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_123_cast = add(x = var_1885_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_123_cast")];
tensor<int32, [3]> var_1890 = const()[name = tensor<string, []>("op_1890"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_325_cast = reshape(shape = var_1890, x = attn_weights_123_cast)[name = tensor<string, []>("input_325_cast")];
tensor<fp16, [16, 77, 77]> input_327_cast = softmax(axis = var_5, x = input_325_cast)[name = tensor<string, []>("input_327_cast")];
tensor<bool, []> attn_output_121_transpose_x_0 = const()[name = tensor<string, []>("attn_output_121_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_121_transpose_y_0 = const()[name = tensor<string, []>("attn_output_121_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_121_cast = matmul(transpose_x = attn_output_121_transpose_x_0, transpose_y = attn_output_121_transpose_y_0, x = input_327_cast, y = value_states_83_cast)[name = tensor<string, []>("attn_output_121_cast")];
tensor<int32, [4]> var_1895 = const()[name = tensor<string, []>("op_1895"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_123_cast = reshape(shape = var_1895, x = attn_output_121_cast)[name = tensor<string, []>("attn_output_123_cast")];
tensor<int32, [4]> attn_output_125_perm_0 = const()[name = tensor<string, []>("attn_output_125_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1898 = const()[name = tensor<string, []>("op_1898"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_10 = transpose(perm = attn_output_125_perm_0, x = attn_output_123_cast)[name = tensor<string, []>("transpose_10")];
tensor<fp16, [1, 77, 1024]> input_329_cast = reshape(shape = var_1898, x = transpose_10)[name = tensor<string, []>("input_329_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292843584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293630080))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293630272)))];
tensor<fp16, [1, 77, 1024]> hidden_states_123_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized, x = input_329_cast)[name = tensor<string, []>("hidden_states_123_cast")];
tensor<fp16, [1, 77, 1024]> input_331_cast = add(x = input_323_cast, y = hidden_states_123_cast)[name = tensor<string, []>("input_331_cast")];
tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293632384)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293634496)))];
tensor<fp16, [1, 77, 1024]> input_333_cast = layer_norm(axes = input_333_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16, x = input_331_cast)[name = tensor<string, []>("input_333_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293636608))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296782400))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296782592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296785728))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_335_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized, x = input_333_cast)[name = tensor<string, []>("input_335_cast")];
tensor<string, []> input_337_mode_0 = const()[name = tensor<string, []>("input_337_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_337_cast = gelu(mode = input_337_mode_0, x = input_335_cast)[name = tensor<string, []>("input_337_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296785920))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299931712))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299931904)))];
tensor<fp16, [1, 77, 1024]> hidden_states_125_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized, x = input_337_cast)[name = tensor<string, []>("hidden_states_125_cast")];
tensor<fp16, [1, 77, 1024]> input_339_cast = add(x = input_331_cast, y = hidden_states_125_cast)[name = tensor<string, []>("input_339_cast")];
tensor<int32, [1]> hidden_states_127_axes_0 = const()[name = tensor<string, []>("hidden_states_127_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299934016)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299936128)))];
tensor<fp16, [1, 77, 1024]> hidden_states_127_cast = layer_norm(axes = hidden_states_127_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16, x = input_339_cast)[name = tensor<string, []>("hidden_states_127_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299938240))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300724736))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300724928)))];
tensor<fp16, [1, 77, 1024]> var_1936_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("op_1936_cast")];
tensor<fp16, []> var_1937_to_fp16 = const()[name = tensor<string, []>("op_1937_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_131_cast = mul(x = var_1936_cast, y = var_1937_to_fp16)[name = tensor<string, []>("tensor_131_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300727040))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301513536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301513728)))];
tensor<fp16, [1, 77, 1024]> tensor_127_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("tensor_127_cast")];
tensor<int32, [4]> var_1942 = const()[name = tensor<string, []>("op_1942"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1943_cast = reshape(shape = var_1942, x = tensor_127_cast)[name = tensor<string, []>("op_1943_cast")];
tensor<int32, [4]> var_1944_perm_0 = const()[name = tensor<string, []>("op_1944_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301515840))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302302336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302302528)))];
tensor<fp16, [1, 77, 1024]> tensor_129_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("tensor_129_cast")];
tensor<int32, [4]> var_1949 = const()[name = tensor<string, []>("op_1949"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1950_cast = reshape(shape = var_1949, x = tensor_129_cast)[name = tensor<string, []>("op_1950_cast")];
tensor<int32, [4]> var_1951_perm_0 = const()[name = tensor<string, []>("op_1951_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_1958 = const()[name = tensor<string, []>("op_1958"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_1959_cast = reshape(shape = var_1958, x = tensor_131_cast)[name = tensor<string, []>("op_1959_cast")];
tensor<int32, [4]> var_1960_perm_0 = const()[name = tensor<string, []>("op_1960_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1962 = const()[name = tensor<string, []>("op_1962"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_9 = transpose(perm = var_1960_perm_0, x = var_1959_cast)[name = tensor<string, []>("transpose_9")];
tensor<fp16, [16, 77, 64]> query_states_43_cast = reshape(shape = var_1962, x = transpose_9)[name = tensor<string, []>("query_states_43_cast")];
tensor<int32, [3]> var_1964 = const()[name = tensor<string, []>("op_1964"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_8 = transpose(perm = var_1944_perm_0, x = var_1943_cast)[name = tensor<string, []>("transpose_8")];
tensor<fp16, [16, 77, 64]> key_states_87_cast = reshape(shape = var_1964, x = transpose_8)[name = tensor<string, []>("key_states_87_cast")];
tensor<int32, [3]> var_1966 = const()[name = tensor<string, []>("op_1966"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_7 = transpose(perm = var_1951_perm_0, x = var_1950_cast)[name = tensor<string, []>("transpose_7")];
tensor<fp16, [16, 77, 64]> value_states_87_cast = reshape(shape = var_1966, x = transpose_7)[name = tensor<string, []>("value_states_87_cast")];
tensor<int32, [3]> var_1969_perm_0 = const()[name = tensor<string, []>("op_1969_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_127_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_127_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_127_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_127_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_6 = transpose(perm = var_1969_perm_0, x = key_states_87_cast)[name = tensor<string, []>("transpose_6")];
tensor<fp16, [16, 77, 77]> attn_weights_127_cast = matmul(transpose_x = attn_weights_127_transpose_x_0, transpose_y = attn_weights_127_transpose_y_0, x = query_states_43_cast, y = transpose_6)[name = tensor<string, []>("attn_weights_127_cast")];
tensor<int32, [4]> var_1971 = const()[name = tensor<string, []>("op_1971"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_1972_cast = reshape(shape = var_1971, x = attn_weights_127_cast)[name = tensor<string, []>("op_1972_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_129_cast = add(x = var_1972_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_129_cast")];
tensor<int32, [3]> var_1977 = const()[name = tensor<string, []>("op_1977"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_341_cast = reshape(shape = var_1977, x = attn_weights_129_cast)[name = tensor<string, []>("input_341_cast")];
tensor<fp16, [16, 77, 77]> input_343_cast = softmax(axis = var_5, x = input_341_cast)[name = tensor<string, []>("input_343_cast")];
tensor<bool, []> attn_output_127_transpose_x_0 = const()[name = tensor<string, []>("attn_output_127_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_127_transpose_y_0 = const()[name = tensor<string, []>("attn_output_127_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_127_cast = matmul(transpose_x = attn_output_127_transpose_x_0, transpose_y = attn_output_127_transpose_y_0, x = input_343_cast, y = value_states_87_cast)[name = tensor<string, []>("attn_output_127_cast")];
tensor<int32, [4]> var_1982 = const()[name = tensor<string, []>("op_1982"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_129_cast = reshape(shape = var_1982, x = attn_output_127_cast)[name = tensor<string, []>("attn_output_129_cast")];
tensor<int32, [4]> attn_output_131_perm_0 = const()[name = tensor<string, []>("attn_output_131_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1985 = const()[name = tensor<string, []>("op_1985"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_5 = transpose(perm = attn_output_131_perm_0, x = attn_output_129_cast)[name = tensor<string, []>("transpose_5")];
tensor<fp16, [1, 77, 1024]> input_345_cast = reshape(shape = var_1985, x = transpose_5)[name = tensor<string, []>("input_345_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302304640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303091136))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303091328)))];
tensor<fp16, [1, 77, 1024]> hidden_states_129_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized, x = input_345_cast)[name = tensor<string, []>("hidden_states_129_cast")];
tensor<fp16, [1, 77, 1024]> input_347_cast = add(x = input_339_cast, y = hidden_states_129_cast)[name = tensor<string, []>("input_347_cast")];
tensor<int32, [1]> input_349_axes_0 = const()[name = tensor<string, []>("input_349_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303093440)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303095552)))];
tensor<fp16, [1, 77, 1024]> input_349_cast = layer_norm(axes = input_349_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16, x = input_347_cast)[name = tensor<string, []>("input_349_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303097664))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306243456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306243648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306246784))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_351_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized, x = input_349_cast)[name = tensor<string, []>("input_351_cast")];
tensor<string, []> input_353_mode_0 = const()[name = tensor<string, []>("input_353_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_353_cast = gelu(mode = input_353_mode_0, x = input_351_cast)[name = tensor<string, []>("input_353_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306246976))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309392768))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309392960)))];
tensor<fp16, [1, 77, 1024]> hidden_states_131_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized, x = input_353_cast)[name = tensor<string, []>("hidden_states_131_cast")];
tensor<fp16, [1, 77, 1024]> input_355_cast = add(x = input_347_cast, y = hidden_states_131_cast)[name = tensor<string, []>("input_355_cast")];
tensor<int32, [1]> hidden_states_133_axes_0 = const()[name = tensor<string, []>("hidden_states_133_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309395072)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309397184)))];
tensor<fp16, [1, 77, 1024]> hidden_states_133_cast = layer_norm(axes = hidden_states_133_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16, x = input_355_cast)[name = tensor<string, []>("hidden_states_133_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309399296))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310185792))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310185984)))];
tensor<fp16, [1, 77, 1024]> var_2023_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("op_2023_cast")];
tensor<fp16, []> var_2024_to_fp16 = const()[name = tensor<string, []>("op_2024_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 77, 1024]> tensor_cast = mul(x = var_2023_cast, y = var_2024_to_fp16)[name = tensor<string, []>("tensor_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310188096))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310974592))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310974784)))];
tensor<fp16, [1, 77, 1024]> tensor_133_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("tensor_133_cast")];
tensor<int32, [4]> var_2029 = const()[name = tensor<string, []>("op_2029"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_2030_cast = reshape(shape = var_2029, x = tensor_133_cast)[name = tensor<string, []>("op_2030_cast")];
tensor<int32, [4]> var_2031_perm_0 = const()[name = tensor<string, []>("op_2031_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310976896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311763392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311763584)))];
tensor<fp16, [1, 77, 1024]> tensor_135_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("tensor_135_cast")];
tensor<int32, [4]> var_2036 = const()[name = tensor<string, []>("op_2036"), val = tensor<int32, [4]>([1, -1, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_2037_cast = reshape(shape = var_2036, x = tensor_135_cast)[name = tensor<string, []>("op_2037_cast")];
tensor<int32, [4]> var_2038_perm_0 = const()[name = tensor<string, []>("op_2038_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_2045 = const()[name = tensor<string, []>("op_2045"), val = tensor<int32, [4]>([1, 77, 16, 64])];
tensor<fp16, [1, 77, 16, 64]> var_2046_cast = reshape(shape = var_2045, x = tensor_cast)[name = tensor<string, []>("op_2046_cast")];
tensor<int32, [4]> var_2047_perm_0 = const()[name = tensor<string, []>("op_2047_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_2049 = const()[name = tensor<string, []>("op_2049"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_4 = transpose(perm = var_2047_perm_0, x = var_2046_cast)[name = tensor<string, []>("transpose_4")];
tensor<fp16, [16, 77, 64]> query_states_cast = reshape(shape = var_2049, x = transpose_4)[name = tensor<string, []>("query_states_cast")];
tensor<int32, [3]> var_2051 = const()[name = tensor<string, []>("op_2051"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_3 = transpose(perm = var_2031_perm_0, x = var_2030_cast)[name = tensor<string, []>("transpose_3")];
tensor<fp16, [16, 77, 64]> key_states_cast = reshape(shape = var_2051, x = transpose_3)[name = tensor<string, []>("key_states_cast")];
tensor<int32, [3]> var_2053 = const()[name = tensor<string, []>("op_2053"), val = tensor<int32, [3]>([16, -1, 64])];
tensor<fp16, [1, 16, 77, 64]> transpose_2 = transpose(perm = var_2038_perm_0, x = var_2037_cast)[name = tensor<string, []>("transpose_2")];
tensor<fp16, [16, 77, 64]> value_states_cast = reshape(shape = var_2053, x = transpose_2)[name = tensor<string, []>("value_states_cast")];
tensor<int32, [3]> var_2056_perm_0 = const()[name = tensor<string, []>("op_2056_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<bool, []> attn_weights_133_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_weights_133_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 64, 77]> transpose_1 = transpose(perm = var_2056_perm_0, x = key_states_cast)[name = tensor<string, []>("transpose_1")];
tensor<fp16, [16, 77, 77]> attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = query_states_cast, y = transpose_1)[name = tensor<string, []>("attn_weights_133_cast")];
tensor<int32, [4]> var_2058 = const()[name = tensor<string, []>("op_2058"), val = tensor<int32, [4]>([1, 16, 77, 77])];
tensor<fp16, [1, 16, 77, 77]> var_2059_cast = reshape(shape = var_2058, x = attn_weights_133_cast)[name = tensor<string, []>("op_2059_cast")];
tensor<fp16, [1, 16, 77, 77]> attn_weights_135_cast = add(x = var_2059_cast, y = causal_attention_mask_to_fp16_palettized)[name = tensor<string, []>("attn_weights_135_cast")];
tensor<int32, [3]> var_2064 = const()[name = tensor<string, []>("op_2064"), val = tensor<int32, [3]>([16, 77, 77])];
tensor<fp16, [16, 77, 77]> input_357_cast = reshape(shape = var_2064, x = attn_weights_135_cast)[name = tensor<string, []>("input_357_cast")];
tensor<fp16, [16, 77, 77]> input_359_cast = softmax(axis = var_5, x = input_357_cast)[name = tensor<string, []>("input_359_cast")];
tensor<bool, []> attn_output_133_transpose_x_0 = const()[name = tensor<string, []>("attn_output_133_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_output_133_transpose_y_0 = const()[name = tensor<string, []>("attn_output_133_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [16, 77, 64]> attn_output_133_cast = matmul(transpose_x = attn_output_133_transpose_x_0, transpose_y = attn_output_133_transpose_y_0, x = input_359_cast, y = value_states_cast)[name = tensor<string, []>("attn_output_133_cast")];
tensor<int32, [4]> var_2069 = const()[name = tensor<string, []>("op_2069"), val = tensor<int32, [4]>([1, 16, 77, 64])];
tensor<fp16, [1, 16, 77, 64]> attn_output_135_cast = reshape(shape = var_2069, x = attn_output_133_cast)[name = tensor<string, []>("attn_output_135_cast")];
tensor<int32, [4]> attn_output_perm_0 = const()[name = tensor<string, []>("attn_output_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_2072 = const()[name = tensor<string, []>("op_2072"), val = tensor<int32, [3]>([1, 77, 1024])];
tensor<fp16, [1, 77, 16, 64]> transpose_0 = transpose(perm = attn_output_perm_0, x = attn_output_135_cast)[name = tensor<string, []>("transpose_0")];
tensor<fp16, [1, 77, 1024]> input_361_cast = reshape(shape = var_2072, x = transpose_0)[name = tensor<string, []>("input_361_cast")];
tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311765696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312552192))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312552384)))];
tensor<fp16, [1, 77, 1024]> hidden_states_135_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized, x = input_361_cast)[name = tensor<string, []>("hidden_states_135_cast")];
tensor<fp16, [1, 77, 1024]> input_363_cast = add(x = input_355_cast, y = hidden_states_135_cast)[name = tensor<string, []>("input_363_cast")];
tensor<int32, [1]> input_365_axes_0 = const()[name = tensor<string, []>("input_365_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312554496)))];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312556608)))];
tensor<fp16, [1, 77, 1024]> input_365_cast = layer_norm(axes = input_365_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16, x = input_363_cast)[name = tensor<string, []>("input_365_cast")];
tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312558720))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315704512))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315704704))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315707840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
tensor<fp16, [1, 77, 4096]> input_367_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized, x = input_365_cast)[name = tensor<string, []>("input_367_cast")];
tensor<string, []> input_369_mode_0 = const()[name = tensor<string, []>("input_369_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 77, 4096]> input_369_cast = gelu(mode = input_369_mode_0, x = input_367_cast)[name = tensor<string, []>("input_369_cast")];
tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315708032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318853824))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318854016)))];
tensor<fp16, [1, 77, 1024]> hidden_states_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized, x = input_369_cast)[name = tensor<string, []>("hidden_states_cast")];
tensor<fp16, [1, 77, 1024]> input_cast = add(x = input_363_cast, y = hidden_states_cast)[name = tensor<string, []>("input_cast")];
tensor<int32, [1]> last_hidden_state_axes_0 = const()[name = tensor<string, []>("last_hidden_state_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1024]> text_encoder_text_model_final_layer_norm_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318856128)))];
tensor<fp16, [1024]> text_encoder_text_model_final_layer_norm_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318858240)))];
tensor<fp16, [1, 77, 1024]> last_hidden_state_cast = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_cast)[name = tensor<string, []>("last_hidden_state_cast")];
tensor<string, []> last_hidden_state_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("last_hidden_state_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
tensor<int32, [1]> var_2097 = const()[name = tensor<string, []>("op_2097"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> var_2099 = reduce_argmax(axis = var_5, keep_dims = var_6, x = cast_2)[name = tensor<string, []>("op_2099")];
tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(1)];
tensor<int32, [1, 2]> stack_0 = stack(axis = stack_0_axis_0, values = (var_2097, var_2099))[name = tensor<string, []>("stack_0")];
tensor<int32, []> var_2101_transpose_batch_dims_0 = const()[name = tensor<string, []>("op_2101_transpose_batch_dims_0"), val = tensor<int32, []>(0)];
tensor<fp16, [1, 1024]> var_2101_transpose_cast = gather_nd(batch_dims = var_2101_transpose_batch_dims_0, indices = stack_0, x = last_hidden_state_cast)[name = tensor<string, []>("op_2101_transpose_cast")];
tensor<string, []> var_2101_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_2101_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
tensor<fp32, [1, 77, 1024]> last_hidden_state = cast(dtype = last_hidden_state_cast_to_fp32_dtype_0, x = last_hidden_state_cast)[name = tensor<string, []>("cast_0")];
tensor<fp32, [1, 1024]> pooled_outputs = cast(dtype = var_2101_cast_to_fp32_dtype_0, x = var_2101_transpose_cast)[name = tensor<string, []>("cast_1")];
} -> (last_hidden_state, pooled_outputs);
} |