| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] |
| { |
| func main<ios17>(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<bool, []> inputs_embeds_validate_indices_0 = const()[name = tensor<string, []>("inputs_embeds_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [49408, 1280]> 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, 1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<int32, [1, 77]> cast_1 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor<string, []>("cast_3")]; |
| tensor<fp16, [1, 77, 1280]> inputs_embeds_cast_fp16 = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_1, validate_indices = inputs_embeds_validate_indices_0, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor<string, []>("inputs_embeds_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> position_embeddings_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [98560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126484608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126583232))), name = tensor<string, []>("position_embeddings_to_fp16_palettized"), shape = tensor<uint32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 1280]> input_3_cast_fp16 = add(x = inputs_embeds_cast_fp16, y = position_embeddings_to_fp16_palettized)[name = tensor<string, []>("input_3_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_1_axes_0 = const()[name = tensor<string, []>("hidden_states_1_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126583808)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126586432)))]; |
| tensor<fp16, []> var_15_to_fp16 = const()[name = tensor<string, []>("op_15_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_1_cast_fp16 = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("hidden_states_1_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126589056))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128227520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128228096)))]; |
| tensor<fp16, [1, 77, 1280]> linear_0_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_0_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128230720))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129869184))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129869760)))]; |
| tensor<fp16, [1, 77, 1280]> linear_1_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_1_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129872384))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131510848))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131511424)))]; |
| tensor<fp16, [1, 77, 1280]> linear_2_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_2_cast_fp16")]; |
| tensor<int32, [4]> var_155 = const()[name = tensor<string, []>("op_155"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_156_cast_fp16 = reshape(shape = var_155, x = linear_0_cast_fp16)[name = tensor<string, []>("op_156_cast_fp16")]; |
| tensor<int32, [4]> var_158 = const()[name = tensor<string, []>("op_158"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_159_cast_fp16 = reshape(shape = var_158, x = linear_1_cast_fp16)[name = tensor<string, []>("op_159_cast_fp16")]; |
| tensor<int32, [4]> var_161 = const()[name = tensor<string, []>("op_161"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_162_cast_fp16 = reshape(shape = var_161, x = linear_2_cast_fp16)[name = tensor<string, []>("op_162_cast_fp16")]; |
| tensor<int32, [4]> value_states_3_perm_0 = const()[name = tensor<string, []>("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, []> var_17_to_fp16 = const()[name = tensor<string, []>("op_17_to_fp16"), val = tensor<fp16, []>(0x1p-3)]; |
| tensor<fp16, [1, 77, 20, 64]> mul_0_cast_fp16 = mul(x = var_156_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")]; |
| tensor<bool, []> matmul_0_transpose_y_0 = const()[name = tensor<string, []>("matmul_0_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_0_transpose_x_0 = const()[name = tensor<string, []>("matmul_0_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_128_perm_0 = const()[name = tensor<string, []>("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_129_perm_0 = const()[name = tensor<string, []>("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_129 = transpose(perm = transpose_129_perm_0, x = var_159_cast_fp16)[name = tensor<string, []>("transpose_318")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_128 = transpose(perm = transpose_128_perm_0, x = mul_0_cast_fp16)[name = tensor<string, []>("transpose_319")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_128, y = transpose_129)[name = tensor<string, []>("matmul_0_cast_fp16")]; |
| tensor<fp16, [1, 1, 77, 77]> op_59_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5929]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131514048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131520064))), name = tensor<string, []>("op_59_to_fp16_palettized"), shape = tensor<uint32, [4]>([1, 1, 77, 77])]; |
| tensor<fp16, [1, 20, 77, 77]> add_0_cast_fp16 = add(x = matmul_0_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_0_cast_fp16")]; |
| tensor<int32, []> softmax_0_axis_0 = const()[name = tensor<string, []>("softmax_0_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_0_cast_fp16 = softmax(axis = softmax_0_axis_0, x = add_0_cast_fp16)[name = tensor<string, []>("softmax_0_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_162_cast_fp16)[name = tensor<string, []>("transpose_317")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = softmax_0_cast_fp16, y = value_states_3_cast_fp16)[name = tensor<string, []>("attn_output_1_cast_fp16")]; |
| tensor<int32, [4]> attn_output_3_perm_0 = const()[name = tensor<string, []>("attn_output_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_166 = const()[name = tensor<string, []>("op_166"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_3_cast_fp16 = transpose(perm = attn_output_3_perm_0, x = attn_output_1_cast_fp16)[name = tensor<string, []>("transpose_316")]; |
| tensor<fp16, [1, 77, 1280]> input_5_cast_fp16 = reshape(shape = var_166, x = attn_output_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131520640))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133159104))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133159680)))]; |
| tensor<fp16, [1, 77, 1280]> linear_3_cast_fp16 = 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_5_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_7_cast_fp16 = add(x = input_3_cast_fp16, y = linear_3_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")]; |
| tensor<int32, [1]> input_9_axes_0 = const()[name = tensor<string, []>("input_9_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133162304)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133164928)))]; |
| tensor<fp16, [1, 77, 1280]> input_9_cast_fp16 = layer_norm(axes = input_9_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133167552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139721216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139721792)))]; |
| tensor<fp16, [1, 77, 5120]> linear_4_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_9_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")]; |
| tensor<string, []> input_13_mode_0 = const()[name = tensor<string, []>("input_13_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = linear_4_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139732096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146285760))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146286336)))]; |
| tensor<fp16, [1, 77, 1280]> linear_5_cast_fp16 = 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_13_cast_fp16)[name = tensor<string, []>("linear_5_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_15_cast_fp16 = add(x = input_7_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_7_axes_0 = const()[name = tensor<string, []>("hidden_states_7_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146288960)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146291584)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_7_cast_fp16 = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("hidden_states_7_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146294208))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147932672))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147933248)))]; |
| tensor<fp16, [1, 77, 1280]> linear_6_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_6_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147935872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149574336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149574912)))]; |
| tensor<fp16, [1, 77, 1280]> linear_7_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_7_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149577536))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151216000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151216576)))]; |
| tensor<fp16, [1, 77, 1280]> linear_8_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_8_cast_fp16")]; |
| tensor<int32, [4]> var_210 = const()[name = tensor<string, []>("op_210"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_211_cast_fp16 = reshape(shape = var_210, x = linear_6_cast_fp16)[name = tensor<string, []>("op_211_cast_fp16")]; |
| tensor<int32, [4]> var_213 = const()[name = tensor<string, []>("op_213"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_214_cast_fp16 = reshape(shape = var_213, x = linear_7_cast_fp16)[name = tensor<string, []>("op_214_cast_fp16")]; |
| tensor<int32, [4]> var_216 = const()[name = tensor<string, []>("op_216"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_217_cast_fp16 = reshape(shape = var_216, x = linear_8_cast_fp16)[name = tensor<string, []>("op_217_cast_fp16")]; |
| tensor<int32, [4]> value_states_7_perm_0 = const()[name = tensor<string, []>("value_states_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_1_cast_fp16")]; |
| tensor<bool, []> matmul_1_transpose_y_0 = const()[name = tensor<string, []>("matmul_1_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_1_transpose_x_0 = const()[name = tensor<string, []>("matmul_1_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_130_perm_0 = const()[name = tensor<string, []>("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_131_perm_0 = const()[name = tensor<string, []>("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_131 = transpose(perm = transpose_131_perm_0, x = var_214_cast_fp16)[name = tensor<string, []>("transpose_314")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_130 = transpose(perm = transpose_130_perm_0, x = mul_1_cast_fp16)[name = tensor<string, []>("transpose_315")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = transpose_130, y = transpose_131)[name = tensor<string, []>("matmul_1_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_1_cast_fp16 = add(x = matmul_1_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_1_cast_fp16")]; |
| tensor<int32, []> softmax_1_axis_0 = const()[name = tensor<string, []>("softmax_1_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_1_cast_fp16 = softmax(axis = softmax_1_axis_0, x = add_1_cast_fp16)[name = tensor<string, []>("softmax_1_cast_fp16")]; |
| tensor<bool, []> attn_output_5_transpose_x_0 = const()[name = tensor<string, []>("attn_output_5_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_5_transpose_y_0 = const()[name = tensor<string, []>("attn_output_5_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_7_cast_fp16 = transpose(perm = value_states_7_perm_0, x = var_217_cast_fp16)[name = tensor<string, []>("transpose_313")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_5_cast_fp16 = matmul(transpose_x = attn_output_5_transpose_x_0, transpose_y = attn_output_5_transpose_y_0, x = softmax_1_cast_fp16, y = value_states_7_cast_fp16)[name = tensor<string, []>("attn_output_5_cast_fp16")]; |
| tensor<int32, [4]> attn_output_7_perm_0 = const()[name = tensor<string, []>("attn_output_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_221 = const()[name = tensor<string, []>("op_221"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_7_cast_fp16 = transpose(perm = attn_output_7_perm_0, x = attn_output_5_cast_fp16)[name = tensor<string, []>("transpose_312")]; |
| tensor<fp16, [1, 77, 1280]> input_17_cast_fp16 = reshape(shape = var_221, x = attn_output_7_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151219200))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152857664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152858240)))]; |
| tensor<fp16, [1, 77, 1280]> linear_9_cast_fp16 = 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_17_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_19_cast_fp16 = add(x = input_15_cast_fp16, y = linear_9_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")]; |
| tensor<int32, [1]> input_21_axes_0 = const()[name = tensor<string, []>("input_21_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152860864)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152863488)))]; |
| tensor<fp16, [1, 77, 1280]> input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152866112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159419776))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159420352)))]; |
| tensor<fp16, [1, 77, 5120]> linear_10_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")]; |
| tensor<string, []> input_25_mode_0 = const()[name = tensor<string, []>("input_25_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_10_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159430656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165984320))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165984896)))]; |
| tensor<fp16, [1, 77, 1280]> linear_11_cast_fp16 = 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_25_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_27_cast_fp16 = add(x = input_19_cast_fp16, y = linear_11_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_13_axes_0 = const()[name = tensor<string, []>("hidden_states_13_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165987520)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165990144)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_13_cast_fp16 = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("hidden_states_13_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165992768))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167631232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167631808)))]; |
| tensor<fp16, [1, 77, 1280]> linear_12_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_12_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167634432))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169272896))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169273472)))]; |
| tensor<fp16, [1, 77, 1280]> linear_13_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_13_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169276096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170914560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170915136)))]; |
| tensor<fp16, [1, 77, 1280]> linear_14_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_14_cast_fp16")]; |
| tensor<int32, [4]> var_265 = const()[name = tensor<string, []>("op_265"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_266_cast_fp16 = reshape(shape = var_265, x = linear_12_cast_fp16)[name = tensor<string, []>("op_266_cast_fp16")]; |
| tensor<int32, [4]> var_268 = const()[name = tensor<string, []>("op_268"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_269_cast_fp16 = reshape(shape = var_268, x = linear_13_cast_fp16)[name = tensor<string, []>("op_269_cast_fp16")]; |
| tensor<int32, [4]> var_271 = const()[name = tensor<string, []>("op_271"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_272_cast_fp16 = reshape(shape = var_271, x = linear_14_cast_fp16)[name = tensor<string, []>("op_272_cast_fp16")]; |
| tensor<int32, [4]> value_states_11_perm_0 = const()[name = tensor<string, []>("value_states_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_2_cast_fp16 = mul(x = var_266_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_2_cast_fp16")]; |
| tensor<bool, []> matmul_2_transpose_y_0 = const()[name = tensor<string, []>("matmul_2_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_2_transpose_x_0 = const()[name = tensor<string, []>("matmul_2_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_132_perm_0 = const()[name = tensor<string, []>("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_133_perm_0 = const()[name = tensor<string, []>("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_133 = transpose(perm = transpose_133_perm_0, x = var_269_cast_fp16)[name = tensor<string, []>("transpose_310")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_132 = transpose(perm = transpose_132_perm_0, x = mul_2_cast_fp16)[name = tensor<string, []>("transpose_311")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = transpose_132, y = transpose_133)[name = tensor<string, []>("matmul_2_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_2_cast_fp16 = add(x = matmul_2_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_2_cast_fp16")]; |
| tensor<int32, []> softmax_2_axis_0 = const()[name = tensor<string, []>("softmax_2_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_2_cast_fp16 = softmax(axis = softmax_2_axis_0, x = add_2_cast_fp16)[name = tensor<string, []>("softmax_2_cast_fp16")]; |
| tensor<bool, []> attn_output_9_transpose_x_0 = const()[name = tensor<string, []>("attn_output_9_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_9_transpose_y_0 = const()[name = tensor<string, []>("attn_output_9_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_11_cast_fp16 = transpose(perm = value_states_11_perm_0, x = var_272_cast_fp16)[name = tensor<string, []>("transpose_309")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_0, transpose_y = attn_output_9_transpose_y_0, x = softmax_2_cast_fp16, y = value_states_11_cast_fp16)[name = tensor<string, []>("attn_output_9_cast_fp16")]; |
| 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_276 = const()[name = tensor<string, []>("op_276"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_11_cast_fp16 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast_fp16)[name = tensor<string, []>("transpose_308")]; |
| tensor<fp16, [1, 77, 1280]> input_29_cast_fp16 = reshape(shape = var_276, x = attn_output_11_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170917760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172556224))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172556800)))]; |
| tensor<fp16, [1, 77, 1280]> linear_15_cast_fp16 = 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_29_cast_fp16)[name = tensor<string, []>("linear_15_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_31_cast_fp16 = add(x = input_27_cast_fp16, y = linear_15_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")]; |
| tensor<int32, [1]> input_33_axes_0 = const()[name = tensor<string, []>("input_33_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172559424)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172562048)))]; |
| tensor<fp16, [1, 77, 1280]> input_33_cast_fp16 = layer_norm(axes = input_33_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172564672))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179118336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179118912)))]; |
| tensor<fp16, [1, 77, 5120]> linear_16_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")]; |
| tensor<string, []> input_37_mode_0 = const()[name = tensor<string, []>("input_37_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_37_cast_fp16 = gelu(mode = input_37_mode_0, x = linear_16_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179129216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185682880))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185683456)))]; |
| tensor<fp16, [1, 77, 1280]> linear_17_cast_fp16 = 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_37_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_17_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_19_axes_0 = const()[name = tensor<string, []>("hidden_states_19_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185686080)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185688704)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_19_cast_fp16 = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("hidden_states_19_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185691328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187329792))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187330368)))]; |
| tensor<fp16, [1, 77, 1280]> linear_18_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_18_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187332992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188971456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188972032)))]; |
| tensor<fp16, [1, 77, 1280]> linear_19_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_19_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188974656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190613120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190613696)))]; |
| tensor<fp16, [1, 77, 1280]> linear_20_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_20_cast_fp16")]; |
| tensor<int32, [4]> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_321_cast_fp16 = reshape(shape = var_320, x = linear_18_cast_fp16)[name = tensor<string, []>("op_321_cast_fp16")]; |
| tensor<int32, [4]> var_323 = const()[name = tensor<string, []>("op_323"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_324_cast_fp16 = reshape(shape = var_323, x = linear_19_cast_fp16)[name = tensor<string, []>("op_324_cast_fp16")]; |
| tensor<int32, [4]> var_326 = const()[name = tensor<string, []>("op_326"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_327_cast_fp16 = reshape(shape = var_326, x = linear_20_cast_fp16)[name = tensor<string, []>("op_327_cast_fp16")]; |
| tensor<int32, [4]> value_states_15_perm_0 = const()[name = tensor<string, []>("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_3_cast_fp16 = mul(x = var_321_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_3_cast_fp16")]; |
| tensor<bool, []> matmul_3_transpose_y_0 = const()[name = tensor<string, []>("matmul_3_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_3_transpose_x_0 = const()[name = tensor<string, []>("matmul_3_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_134_perm_0 = const()[name = tensor<string, []>("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_135_perm_0 = const()[name = tensor<string, []>("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_135 = transpose(perm = transpose_135_perm_0, x = var_324_cast_fp16)[name = tensor<string, []>("transpose_306")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_134 = transpose(perm = transpose_134_perm_0, x = mul_3_cast_fp16)[name = tensor<string, []>("transpose_307")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = transpose_134, y = transpose_135)[name = tensor<string, []>("matmul_3_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_3_cast_fp16 = add(x = matmul_3_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_3_cast_fp16")]; |
| tensor<int32, []> softmax_3_axis_0 = const()[name = tensor<string, []>("softmax_3_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_3_cast_fp16 = softmax(axis = softmax_3_axis_0, x = add_3_cast_fp16)[name = tensor<string, []>("softmax_3_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_327_cast_fp16)[name = tensor<string, []>("transpose_305")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_13_cast_fp16 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = softmax_3_cast_fp16, y = value_states_15_cast_fp16)[name = tensor<string, []>("attn_output_13_cast_fp16")]; |
| tensor<int32, [4]> attn_output_15_perm_0 = const()[name = tensor<string, []>("attn_output_15_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_331 = const()[name = tensor<string, []>("op_331"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_15_cast_fp16 = transpose(perm = attn_output_15_perm_0, x = attn_output_13_cast_fp16)[name = tensor<string, []>("transpose_304")]; |
| tensor<fp16, [1, 77, 1280]> input_41_cast_fp16 = reshape(shape = var_331, x = attn_output_15_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190616320))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192254784))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192255360)))]; |
| tensor<fp16, [1, 77, 1280]> linear_21_cast_fp16 = 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_41_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_43_cast_fp16 = add(x = input_39_cast_fp16, y = linear_21_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")]; |
| tensor<int32, [1]> input_45_axes_0 = const()[name = tensor<string, []>("input_45_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192257984)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192260608)))]; |
| tensor<fp16, [1, 77, 1280]> input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192263232))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198816896))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198817472)))]; |
| tensor<fp16, [1, 77, 5120]> linear_22_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")]; |
| tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_22_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198827776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205381440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205382016)))]; |
| tensor<fp16, [1, 77, 1280]> linear_23_cast_fp16 = 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_49_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_51_cast_fp16 = add(x = input_43_cast_fp16, y = linear_23_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_25_axes_0 = const()[name = tensor<string, []>("hidden_states_25_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205384640)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205387264)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_25_cast_fp16 = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_51_cast_fp16)[name = tensor<string, []>("hidden_states_25_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205389888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207028352))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207028928)))]; |
| tensor<fp16, [1, 77, 1280]> linear_24_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_24_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207031552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208670016))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208670592)))]; |
| tensor<fp16, [1, 77, 1280]> linear_25_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_25_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208673216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210311680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210312256)))]; |
| tensor<fp16, [1, 77, 1280]> linear_26_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_26_cast_fp16")]; |
| tensor<int32, [4]> var_375 = const()[name = tensor<string, []>("op_375"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_376_cast_fp16 = reshape(shape = var_375, x = linear_24_cast_fp16)[name = tensor<string, []>("op_376_cast_fp16")]; |
| tensor<int32, [4]> var_378 = const()[name = tensor<string, []>("op_378"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_379_cast_fp16 = reshape(shape = var_378, x = linear_25_cast_fp16)[name = tensor<string, []>("op_379_cast_fp16")]; |
| tensor<int32, [4]> var_381 = const()[name = tensor<string, []>("op_381"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_382_cast_fp16 = reshape(shape = var_381, x = linear_26_cast_fp16)[name = tensor<string, []>("op_382_cast_fp16")]; |
| tensor<int32, [4]> value_states_19_perm_0 = const()[name = tensor<string, []>("value_states_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_4_cast_fp16 = mul(x = var_376_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_4_cast_fp16")]; |
| tensor<bool, []> matmul_4_transpose_y_0 = const()[name = tensor<string, []>("matmul_4_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_4_transpose_x_0 = const()[name = tensor<string, []>("matmul_4_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_136_perm_0 = const()[name = tensor<string, []>("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_137_perm_0 = const()[name = tensor<string, []>("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_137 = transpose(perm = transpose_137_perm_0, x = var_379_cast_fp16)[name = tensor<string, []>("transpose_302")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_136 = transpose(perm = transpose_136_perm_0, x = mul_4_cast_fp16)[name = tensor<string, []>("transpose_303")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_4_cast_fp16 = matmul(transpose_x = matmul_4_transpose_x_0, transpose_y = matmul_4_transpose_y_0, x = transpose_136, y = transpose_137)[name = tensor<string, []>("matmul_4_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_4_cast_fp16 = add(x = matmul_4_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_4_cast_fp16")]; |
| tensor<int32, []> softmax_4_axis_0 = const()[name = tensor<string, []>("softmax_4_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_4_cast_fp16 = softmax(axis = softmax_4_axis_0, x = add_4_cast_fp16)[name = tensor<string, []>("softmax_4_cast_fp16")]; |
| tensor<bool, []> attn_output_17_transpose_x_0 = const()[name = tensor<string, []>("attn_output_17_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_17_transpose_y_0 = const()[name = tensor<string, []>("attn_output_17_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_19_cast_fp16 = transpose(perm = value_states_19_perm_0, x = var_382_cast_fp16)[name = tensor<string, []>("transpose_301")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_0, transpose_y = attn_output_17_transpose_y_0, x = softmax_4_cast_fp16, y = value_states_19_cast_fp16)[name = tensor<string, []>("attn_output_17_cast_fp16")]; |
| tensor<int32, [4]> attn_output_19_perm_0 = const()[name = tensor<string, []>("attn_output_19_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_386 = const()[name = tensor<string, []>("op_386"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_19_cast_fp16 = transpose(perm = attn_output_19_perm_0, x = attn_output_17_cast_fp16)[name = tensor<string, []>("transpose_300")]; |
| tensor<fp16, [1, 77, 1280]> input_53_cast_fp16 = reshape(shape = var_386, x = attn_output_19_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210314880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211953344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211953920)))]; |
| tensor<fp16, [1, 77, 1280]> linear_27_cast_fp16 = 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_53_cast_fp16)[name = tensor<string, []>("linear_27_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_55_cast_fp16 = add(x = input_51_cast_fp16, y = linear_27_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")]; |
| tensor<int32, [1]> input_57_axes_0 = const()[name = tensor<string, []>("input_57_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211956544)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211959168)))]; |
| tensor<fp16, [1, 77, 1280]> input_57_cast_fp16 = layer_norm(axes = input_57_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211961792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218515456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218516032)))]; |
| tensor<fp16, [1, 77, 5120]> linear_28_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor<string, []>("linear_28_cast_fp16")]; |
| tensor<string, []> input_61_mode_0 = const()[name = tensor<string, []>("input_61_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_61_cast_fp16 = gelu(mode = input_61_mode_0, x = linear_28_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218526336))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225080000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225080576)))]; |
| tensor<fp16, [1, 77, 1280]> linear_29_cast_fp16 = 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_61_cast_fp16)[name = tensor<string, []>("linear_29_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_63_cast_fp16 = add(x = input_55_cast_fp16, y = linear_29_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_31_axes_0 = const()[name = tensor<string, []>("hidden_states_31_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225083200)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225085824)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_31_cast_fp16 = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = tensor<string, []>("hidden_states_31_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225088448))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226726912))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226727488)))]; |
| tensor<fp16, [1, 77, 1280]> linear_30_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_30_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226730112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228368576))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228369152)))]; |
| tensor<fp16, [1, 77, 1280]> linear_31_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_31_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228371776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230010240))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230010816)))]; |
| tensor<fp16, [1, 77, 1280]> linear_32_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_32_cast_fp16")]; |
| tensor<int32, [4]> var_430 = const()[name = tensor<string, []>("op_430"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_431_cast_fp16 = reshape(shape = var_430, x = linear_30_cast_fp16)[name = tensor<string, []>("op_431_cast_fp16")]; |
| tensor<int32, [4]> var_433 = const()[name = tensor<string, []>("op_433"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_434_cast_fp16 = reshape(shape = var_433, x = linear_31_cast_fp16)[name = tensor<string, []>("op_434_cast_fp16")]; |
| tensor<int32, [4]> var_436 = const()[name = tensor<string, []>("op_436"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_437_cast_fp16 = reshape(shape = var_436, x = linear_32_cast_fp16)[name = tensor<string, []>("op_437_cast_fp16")]; |
| tensor<int32, [4]> value_states_23_perm_0 = const()[name = tensor<string, []>("value_states_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_5_cast_fp16 = mul(x = var_431_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_5_cast_fp16")]; |
| tensor<bool, []> matmul_5_transpose_y_0 = const()[name = tensor<string, []>("matmul_5_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_5_transpose_x_0 = const()[name = tensor<string, []>("matmul_5_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_138_perm_0 = const()[name = tensor<string, []>("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_139_perm_0 = const()[name = tensor<string, []>("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_139 = transpose(perm = transpose_139_perm_0, x = var_434_cast_fp16)[name = tensor<string, []>("transpose_298")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_138 = transpose(perm = transpose_138_perm_0, x = mul_5_cast_fp16)[name = tensor<string, []>("transpose_299")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_5_cast_fp16 = matmul(transpose_x = matmul_5_transpose_x_0, transpose_y = matmul_5_transpose_y_0, x = transpose_138, y = transpose_139)[name = tensor<string, []>("matmul_5_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_5_cast_fp16 = add(x = matmul_5_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_5_cast_fp16")]; |
| tensor<int32, []> softmax_5_axis_0 = const()[name = tensor<string, []>("softmax_5_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_5_cast_fp16 = softmax(axis = softmax_5_axis_0, x = add_5_cast_fp16)[name = tensor<string, []>("softmax_5_cast_fp16")]; |
| tensor<bool, []> attn_output_21_transpose_x_0 = const()[name = tensor<string, []>("attn_output_21_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_21_transpose_y_0 = const()[name = tensor<string, []>("attn_output_21_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_23_cast_fp16 = transpose(perm = value_states_23_perm_0, x = var_437_cast_fp16)[name = tensor<string, []>("transpose_297")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_21_cast_fp16 = matmul(transpose_x = attn_output_21_transpose_x_0, transpose_y = attn_output_21_transpose_y_0, x = softmax_5_cast_fp16, y = value_states_23_cast_fp16)[name = tensor<string, []>("attn_output_21_cast_fp16")]; |
| 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_441 = const()[name = tensor<string, []>("op_441"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_23_cast_fp16 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast_fp16)[name = tensor<string, []>("transpose_296")]; |
| tensor<fp16, [1, 77, 1280]> input_65_cast_fp16 = reshape(shape = var_441, x = attn_output_23_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230013440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231651904))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231652480)))]; |
| tensor<fp16, [1, 77, 1280]> linear_33_cast_fp16 = 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_65_cast_fp16)[name = tensor<string, []>("linear_33_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_67_cast_fp16 = add(x = input_63_cast_fp16, y = linear_33_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")]; |
| tensor<int32, [1]> input_69_axes_0 = const()[name = tensor<string, []>("input_69_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231655104)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231657728)))]; |
| tensor<fp16, [1, 77, 1280]> input_69_cast_fp16 = layer_norm(axes = input_69_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231660352))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238214016))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238214592)))]; |
| tensor<fp16, [1, 77, 5120]> linear_34_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")]; |
| tensor<string, []> input_73_mode_0 = const()[name = tensor<string, []>("input_73_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_34_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238224896))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244778560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244779136)))]; |
| tensor<fp16, [1, 77, 1280]> linear_35_cast_fp16 = 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_73_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_75_cast_fp16 = add(x = input_67_cast_fp16, y = linear_35_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_37_axes_0 = const()[name = tensor<string, []>("hidden_states_37_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244781760)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244784384)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_37_cast_fp16 = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("hidden_states_37_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244787008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246425472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246426048)))]; |
| tensor<fp16, [1, 77, 1280]> linear_36_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_36_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246428672))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248067136))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248067712)))]; |
| tensor<fp16, [1, 77, 1280]> linear_37_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_37_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248070336))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249708800))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249709376)))]; |
| tensor<fp16, [1, 77, 1280]> linear_38_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_38_cast_fp16")]; |
| tensor<int32, [4]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_486_cast_fp16 = reshape(shape = var_485, x = linear_36_cast_fp16)[name = tensor<string, []>("op_486_cast_fp16")]; |
| tensor<int32, [4]> var_488 = const()[name = tensor<string, []>("op_488"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_489_cast_fp16 = reshape(shape = var_488, x = linear_37_cast_fp16)[name = tensor<string, []>("op_489_cast_fp16")]; |
| tensor<int32, [4]> var_491 = const()[name = tensor<string, []>("op_491"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_492_cast_fp16 = reshape(shape = var_491, x = linear_38_cast_fp16)[name = tensor<string, []>("op_492_cast_fp16")]; |
| tensor<int32, [4]> value_states_27_perm_0 = const()[name = tensor<string, []>("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_6_cast_fp16 = mul(x = var_486_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_6_cast_fp16")]; |
| tensor<bool, []> matmul_6_transpose_y_0 = const()[name = tensor<string, []>("matmul_6_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_6_transpose_x_0 = const()[name = tensor<string, []>("matmul_6_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_140_perm_0 = const()[name = tensor<string, []>("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_141_perm_0 = const()[name = tensor<string, []>("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_141 = transpose(perm = transpose_141_perm_0, x = var_489_cast_fp16)[name = tensor<string, []>("transpose_294")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_140 = transpose(perm = transpose_140_perm_0, x = mul_6_cast_fp16)[name = tensor<string, []>("transpose_295")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_6_cast_fp16 = matmul(transpose_x = matmul_6_transpose_x_0, transpose_y = matmul_6_transpose_y_0, x = transpose_140, y = transpose_141)[name = tensor<string, []>("matmul_6_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_6_cast_fp16 = add(x = matmul_6_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_6_cast_fp16")]; |
| tensor<int32, []> softmax_6_axis_0 = const()[name = tensor<string, []>("softmax_6_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_6_cast_fp16 = softmax(axis = softmax_6_axis_0, x = add_6_cast_fp16)[name = tensor<string, []>("softmax_6_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_492_cast_fp16)[name = tensor<string, []>("transpose_293")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = softmax_6_cast_fp16, y = value_states_27_cast_fp16)[name = tensor<string, []>("attn_output_25_cast_fp16")]; |
| tensor<int32, [4]> attn_output_27_perm_0 = const()[name = tensor<string, []>("attn_output_27_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_496 = const()[name = tensor<string, []>("op_496"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_27_cast_fp16 = transpose(perm = attn_output_27_perm_0, x = attn_output_25_cast_fp16)[name = tensor<string, []>("transpose_292")]; |
| tensor<fp16, [1, 77, 1280]> input_77_cast_fp16 = reshape(shape = var_496, x = attn_output_27_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249712000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251350464))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251351040)))]; |
| tensor<fp16, [1, 77, 1280]> linear_39_cast_fp16 = 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_77_cast_fp16)[name = tensor<string, []>("linear_39_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_79_cast_fp16 = add(x = input_75_cast_fp16, y = linear_39_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")]; |
| tensor<int32, [1]> input_81_axes_0 = const()[name = tensor<string, []>("input_81_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251353664)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251356288)))]; |
| tensor<fp16, [1, 77, 1280]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251358912))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257912576))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257913152)))]; |
| tensor<fp16, [1, 77, 5120]> linear_40_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_81_cast_fp16)[name = tensor<string, []>("linear_40_cast_fp16")]; |
| tensor<string, []> input_85_mode_0 = const()[name = tensor<string, []>("input_85_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_85_cast_fp16 = gelu(mode = input_85_mode_0, x = linear_40_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257923456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264477120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264477696)))]; |
| tensor<fp16, [1, 77, 1280]> linear_41_cast_fp16 = 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_85_cast_fp16)[name = tensor<string, []>("linear_41_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_87_cast_fp16 = add(x = input_79_cast_fp16, y = linear_41_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_43_axes_0 = const()[name = tensor<string, []>("hidden_states_43_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264480320)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264482944)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_43_cast_fp16 = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_87_cast_fp16)[name = tensor<string, []>("hidden_states_43_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264485568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266124032))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266124608)))]; |
| tensor<fp16, [1, 77, 1280]> linear_42_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_42_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266127232))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267765696))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267766272)))]; |
| tensor<fp16, [1, 77, 1280]> linear_43_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_43_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267768896))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269407360))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269407936)))]; |
| tensor<fp16, [1, 77, 1280]> linear_44_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_44_cast_fp16")]; |
| tensor<int32, [4]> var_540 = const()[name = tensor<string, []>("op_540"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_541_cast_fp16 = reshape(shape = var_540, x = linear_42_cast_fp16)[name = tensor<string, []>("op_541_cast_fp16")]; |
| tensor<int32, [4]> var_543 = const()[name = tensor<string, []>("op_543"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_544_cast_fp16 = reshape(shape = var_543, x = linear_43_cast_fp16)[name = tensor<string, []>("op_544_cast_fp16")]; |
| tensor<int32, [4]> var_546 = const()[name = tensor<string, []>("op_546"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_547_cast_fp16 = reshape(shape = var_546, x = linear_44_cast_fp16)[name = tensor<string, []>("op_547_cast_fp16")]; |
| tensor<int32, [4]> value_states_31_perm_0 = const()[name = tensor<string, []>("value_states_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_7_cast_fp16 = mul(x = var_541_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_7_cast_fp16")]; |
| tensor<bool, []> matmul_7_transpose_y_0 = const()[name = tensor<string, []>("matmul_7_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_7_transpose_x_0 = const()[name = tensor<string, []>("matmul_7_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_142_perm_0 = const()[name = tensor<string, []>("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_143_perm_0 = const()[name = tensor<string, []>("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_143 = transpose(perm = transpose_143_perm_0, x = var_544_cast_fp16)[name = tensor<string, []>("transpose_290")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_142 = transpose(perm = transpose_142_perm_0, x = mul_7_cast_fp16)[name = tensor<string, []>("transpose_291")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_7_cast_fp16 = matmul(transpose_x = matmul_7_transpose_x_0, transpose_y = matmul_7_transpose_y_0, x = transpose_142, y = transpose_143)[name = tensor<string, []>("matmul_7_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_7_cast_fp16 = add(x = matmul_7_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_7_cast_fp16")]; |
| tensor<int32, []> softmax_7_axis_0 = const()[name = tensor<string, []>("softmax_7_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_7_cast_fp16 = softmax(axis = softmax_7_axis_0, x = add_7_cast_fp16)[name = tensor<string, []>("softmax_7_cast_fp16")]; |
| tensor<bool, []> attn_output_29_transpose_x_0 = const()[name = tensor<string, []>("attn_output_29_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_29_transpose_y_0 = const()[name = tensor<string, []>("attn_output_29_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_31_cast_fp16 = transpose(perm = value_states_31_perm_0, x = var_547_cast_fp16)[name = tensor<string, []>("transpose_289")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_29_cast_fp16 = matmul(transpose_x = attn_output_29_transpose_x_0, transpose_y = attn_output_29_transpose_y_0, x = softmax_7_cast_fp16, y = value_states_31_cast_fp16)[name = tensor<string, []>("attn_output_29_cast_fp16")]; |
| tensor<int32, [4]> attn_output_31_perm_0 = const()[name = tensor<string, []>("attn_output_31_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_551 = const()[name = tensor<string, []>("op_551"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_31_cast_fp16 = transpose(perm = attn_output_31_perm_0, x = attn_output_29_cast_fp16)[name = tensor<string, []>("transpose_288")]; |
| tensor<fp16, [1, 77, 1280]> input_89_cast_fp16 = reshape(shape = var_551, x = attn_output_31_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269410560))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271049024))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271049600)))]; |
| tensor<fp16, [1, 77, 1280]> linear_45_cast_fp16 = 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_89_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_91_cast_fp16 = add(x = input_87_cast_fp16, y = linear_45_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")]; |
| tensor<int32, [1]> input_93_axes_0 = const()[name = tensor<string, []>("input_93_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271052224)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271054848)))]; |
| tensor<fp16, [1, 77, 1280]> input_93_cast_fp16 = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271057472))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277611136))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277611712)))]; |
| tensor<fp16, [1, 77, 5120]> linear_46_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")]; |
| tensor<string, []> input_97_mode_0 = const()[name = tensor<string, []>("input_97_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_97_cast_fp16 = gelu(mode = input_97_mode_0, x = linear_46_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277622016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284175680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284176256)))]; |
| tensor<fp16, [1, 77, 1280]> linear_47_cast_fp16 = 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_97_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_47_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_49_axes_0 = const()[name = tensor<string, []>("hidden_states_49_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284178880)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284181504)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_49_cast_fp16 = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_99_cast_fp16)[name = tensor<string, []>("hidden_states_49_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284184128))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285822592))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285823168)))]; |
| tensor<fp16, [1, 77, 1280]> linear_48_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_48_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285825792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287464256))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287464832)))]; |
| tensor<fp16, [1, 77, 1280]> linear_49_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_49_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287467456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289105920))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289106496)))]; |
| tensor<fp16, [1, 77, 1280]> linear_50_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_50_cast_fp16")]; |
| tensor<int32, [4]> var_595 = const()[name = tensor<string, []>("op_595"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_596_cast_fp16 = reshape(shape = var_595, x = linear_48_cast_fp16)[name = tensor<string, []>("op_596_cast_fp16")]; |
| tensor<int32, [4]> var_598 = const()[name = tensor<string, []>("op_598"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_599_cast_fp16 = reshape(shape = var_598, x = linear_49_cast_fp16)[name = tensor<string, []>("op_599_cast_fp16")]; |
| tensor<int32, [4]> var_601 = const()[name = tensor<string, []>("op_601"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_602_cast_fp16 = reshape(shape = var_601, x = linear_50_cast_fp16)[name = tensor<string, []>("op_602_cast_fp16")]; |
| tensor<int32, [4]> value_states_35_perm_0 = const()[name = tensor<string, []>("value_states_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_8_cast_fp16 = mul(x = var_596_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_8_cast_fp16")]; |
| tensor<bool, []> matmul_8_transpose_y_0 = const()[name = tensor<string, []>("matmul_8_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_8_transpose_x_0 = const()[name = tensor<string, []>("matmul_8_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_144_perm_0 = const()[name = tensor<string, []>("transpose_144_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_145_perm_0 = const()[name = tensor<string, []>("transpose_145_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_145 = transpose(perm = transpose_145_perm_0, x = var_599_cast_fp16)[name = tensor<string, []>("transpose_286")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_144 = transpose(perm = transpose_144_perm_0, x = mul_8_cast_fp16)[name = tensor<string, []>("transpose_287")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_8_cast_fp16 = matmul(transpose_x = matmul_8_transpose_x_0, transpose_y = matmul_8_transpose_y_0, x = transpose_144, y = transpose_145)[name = tensor<string, []>("matmul_8_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_8_cast_fp16 = add(x = matmul_8_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_8_cast_fp16")]; |
| tensor<int32, []> softmax_8_axis_0 = const()[name = tensor<string, []>("softmax_8_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_8_cast_fp16 = softmax(axis = softmax_8_axis_0, x = add_8_cast_fp16)[name = tensor<string, []>("softmax_8_cast_fp16")]; |
| tensor<bool, []> attn_output_33_transpose_x_0 = const()[name = tensor<string, []>("attn_output_33_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_33_transpose_y_0 = const()[name = tensor<string, []>("attn_output_33_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_35_cast_fp16 = transpose(perm = value_states_35_perm_0, x = var_602_cast_fp16)[name = tensor<string, []>("transpose_285")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_0, transpose_y = attn_output_33_transpose_y_0, x = softmax_8_cast_fp16, y = value_states_35_cast_fp16)[name = tensor<string, []>("attn_output_33_cast_fp16")]; |
| 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_606 = const()[name = tensor<string, []>("op_606"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_35_cast_fp16 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast_fp16)[name = tensor<string, []>("transpose_284")]; |
| tensor<fp16, [1, 77, 1280]> input_101_cast_fp16 = reshape(shape = var_606, x = attn_output_35_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289109120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290747584))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290748160)))]; |
| tensor<fp16, [1, 77, 1280]> linear_51_cast_fp16 = 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_101_cast_fp16)[name = tensor<string, []>("linear_51_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_103_cast_fp16 = add(x = input_99_cast_fp16, y = linear_51_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")]; |
| tensor<int32, [1]> input_105_axes_0 = const()[name = tensor<string, []>("input_105_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290750784)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290753408)))]; |
| tensor<fp16, [1, 77, 1280]> input_105_cast_fp16 = layer_norm(axes = input_105_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_103_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290756032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297309696))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297310272)))]; |
| tensor<fp16, [1, 77, 5120]> linear_52_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = tensor<string, []>("linear_52_cast_fp16")]; |
| tensor<string, []> input_109_mode_0 = const()[name = tensor<string, []>("input_109_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_52_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297320576))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303874240))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303874816)))]; |
| tensor<fp16, [1, 77, 1280]> linear_53_cast_fp16 = 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_109_cast_fp16)[name = tensor<string, []>("linear_53_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_111_cast_fp16 = add(x = input_103_cast_fp16, y = linear_53_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_55_axes_0 = const()[name = tensor<string, []>("hidden_states_55_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303877440)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303880064)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_55_cast_fp16 = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("hidden_states_55_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303882688))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305521152))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305521728)))]; |
| tensor<fp16, [1, 77, 1280]> linear_54_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_54_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305524352))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307162816))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307163392)))]; |
| tensor<fp16, [1, 77, 1280]> linear_55_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_55_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307166016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308804480))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308805056)))]; |
| tensor<fp16, [1, 77, 1280]> linear_56_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_56_cast_fp16")]; |
| tensor<int32, [4]> var_650 = const()[name = tensor<string, []>("op_650"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_651_cast_fp16 = reshape(shape = var_650, x = linear_54_cast_fp16)[name = tensor<string, []>("op_651_cast_fp16")]; |
| tensor<int32, [4]> var_653 = const()[name = tensor<string, []>("op_653"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_654_cast_fp16 = reshape(shape = var_653, x = linear_55_cast_fp16)[name = tensor<string, []>("op_654_cast_fp16")]; |
| tensor<int32, [4]> var_656 = const()[name = tensor<string, []>("op_656"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_657_cast_fp16 = reshape(shape = var_656, x = linear_56_cast_fp16)[name = tensor<string, []>("op_657_cast_fp16")]; |
| tensor<int32, [4]> value_states_39_perm_0 = const()[name = tensor<string, []>("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_9_cast_fp16 = mul(x = var_651_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_9_cast_fp16")]; |
| tensor<bool, []> matmul_9_transpose_y_0 = const()[name = tensor<string, []>("matmul_9_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_9_transpose_x_0 = const()[name = tensor<string, []>("matmul_9_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_146_perm_0 = const()[name = tensor<string, []>("transpose_146_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_147_perm_0 = const()[name = tensor<string, []>("transpose_147_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_147 = transpose(perm = transpose_147_perm_0, x = var_654_cast_fp16)[name = tensor<string, []>("transpose_282")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_146 = transpose(perm = transpose_146_perm_0, x = mul_9_cast_fp16)[name = tensor<string, []>("transpose_283")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_9_cast_fp16 = matmul(transpose_x = matmul_9_transpose_x_0, transpose_y = matmul_9_transpose_y_0, x = transpose_146, y = transpose_147)[name = tensor<string, []>("matmul_9_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_9_cast_fp16 = add(x = matmul_9_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_9_cast_fp16")]; |
| tensor<int32, []> softmax_9_axis_0 = const()[name = tensor<string, []>("softmax_9_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_9_cast_fp16 = softmax(axis = softmax_9_axis_0, x = add_9_cast_fp16)[name = tensor<string, []>("softmax_9_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_657_cast_fp16)[name = tensor<string, []>("transpose_281")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_37_cast_fp16 = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = softmax_9_cast_fp16, y = value_states_39_cast_fp16)[name = tensor<string, []>("attn_output_37_cast_fp16")]; |
| tensor<int32, [4]> attn_output_39_perm_0 = const()[name = tensor<string, []>("attn_output_39_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_661 = const()[name = tensor<string, []>("op_661"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_39_cast_fp16 = transpose(perm = attn_output_39_perm_0, x = attn_output_37_cast_fp16)[name = tensor<string, []>("transpose_280")]; |
| tensor<fp16, [1, 77, 1280]> input_113_cast_fp16 = reshape(shape = var_661, x = attn_output_39_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308807680))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310446144))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310446720)))]; |
| tensor<fp16, [1, 77, 1280]> linear_57_cast_fp16 = 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_113_cast_fp16)[name = tensor<string, []>("linear_57_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_115_cast_fp16 = add(x = input_111_cast_fp16, y = linear_57_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")]; |
| tensor<int32, [1]> input_117_axes_0 = const()[name = tensor<string, []>("input_117_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310449344)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310451968)))]; |
| tensor<fp16, [1, 77, 1280]> input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_115_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310454592))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317008256))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317008832)))]; |
| tensor<fp16, [1, 77, 5120]> linear_58_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")]; |
| tensor<string, []> input_121_mode_0 = const()[name = tensor<string, []>("input_121_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_121_cast_fp16 = gelu(mode = input_121_mode_0, x = linear_58_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317019136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323572800))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323573376)))]; |
| tensor<fp16, [1, 77, 1280]> linear_59_cast_fp16 = 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_121_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_123_cast_fp16 = add(x = input_115_cast_fp16, y = linear_59_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_61_axes_0 = const()[name = tensor<string, []>("hidden_states_61_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323576000)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323578624)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_61_cast_fp16 = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("hidden_states_61_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323581248))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325219712))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325220288)))]; |
| tensor<fp16, [1, 77, 1280]> linear_60_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_60_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325222912))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326861376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326861952)))]; |
| tensor<fp16, [1, 77, 1280]> linear_61_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_61_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326864576))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328503040))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328503616)))]; |
| tensor<fp16, [1, 77, 1280]> linear_62_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_62_cast_fp16")]; |
| tensor<int32, [4]> var_705 = const()[name = tensor<string, []>("op_705"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_706_cast_fp16 = reshape(shape = var_705, x = linear_60_cast_fp16)[name = tensor<string, []>("op_706_cast_fp16")]; |
| tensor<int32, [4]> var_708 = const()[name = tensor<string, []>("op_708"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_709_cast_fp16 = reshape(shape = var_708, x = linear_61_cast_fp16)[name = tensor<string, []>("op_709_cast_fp16")]; |
| tensor<int32, [4]> var_711 = const()[name = tensor<string, []>("op_711"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_712_cast_fp16 = reshape(shape = var_711, x = linear_62_cast_fp16)[name = tensor<string, []>("op_712_cast_fp16")]; |
| tensor<int32, [4]> value_states_43_perm_0 = const()[name = tensor<string, []>("value_states_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_10_cast_fp16 = mul(x = var_706_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_10_cast_fp16")]; |
| tensor<bool, []> matmul_10_transpose_y_0 = const()[name = tensor<string, []>("matmul_10_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_10_transpose_x_0 = const()[name = tensor<string, []>("matmul_10_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_148_perm_0 = const()[name = tensor<string, []>("transpose_148_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_149_perm_0 = const()[name = tensor<string, []>("transpose_149_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_149 = transpose(perm = transpose_149_perm_0, x = var_709_cast_fp16)[name = tensor<string, []>("transpose_278")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_148 = transpose(perm = transpose_148_perm_0, x = mul_10_cast_fp16)[name = tensor<string, []>("transpose_279")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_10_cast_fp16 = matmul(transpose_x = matmul_10_transpose_x_0, transpose_y = matmul_10_transpose_y_0, x = transpose_148, y = transpose_149)[name = tensor<string, []>("matmul_10_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_10_cast_fp16 = add(x = matmul_10_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_10_cast_fp16")]; |
| tensor<int32, []> softmax_10_axis_0 = const()[name = tensor<string, []>("softmax_10_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_10_cast_fp16 = softmax(axis = softmax_10_axis_0, x = add_10_cast_fp16)[name = tensor<string, []>("softmax_10_cast_fp16")]; |
| tensor<bool, []> attn_output_41_transpose_x_0 = const()[name = tensor<string, []>("attn_output_41_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_41_transpose_y_0 = const()[name = tensor<string, []>("attn_output_41_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_43_cast_fp16 = transpose(perm = value_states_43_perm_0, x = var_712_cast_fp16)[name = tensor<string, []>("transpose_277")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_0, transpose_y = attn_output_41_transpose_y_0, x = softmax_10_cast_fp16, y = value_states_43_cast_fp16)[name = tensor<string, []>("attn_output_41_cast_fp16")]; |
| tensor<int32, [4]> attn_output_43_perm_0 = const()[name = tensor<string, []>("attn_output_43_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_716 = const()[name = tensor<string, []>("op_716"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_43_cast_fp16 = transpose(perm = attn_output_43_perm_0, x = attn_output_41_cast_fp16)[name = tensor<string, []>("transpose_276")]; |
| tensor<fp16, [1, 77, 1280]> input_125_cast_fp16 = reshape(shape = var_716, x = attn_output_43_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328506240))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330144704))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330145280)))]; |
| tensor<fp16, [1, 77, 1280]> linear_63_cast_fp16 = 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_125_cast_fp16)[name = tensor<string, []>("linear_63_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_127_cast_fp16 = add(x = input_123_cast_fp16, y = linear_63_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")]; |
| tensor<int32, [1]> input_129_axes_0 = const()[name = tensor<string, []>("input_129_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330147904)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330150528)))]; |
| tensor<fp16, [1, 77, 1280]> input_129_cast_fp16 = layer_norm(axes = input_129_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_127_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330153152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336706816))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336707392)))]; |
| tensor<fp16, [1, 77, 5120]> linear_64_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = tensor<string, []>("linear_64_cast_fp16")]; |
| tensor<string, []> input_133_mode_0 = const()[name = tensor<string, []>("input_133_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_64_cast_fp16)[name = tensor<string, []>("input_133_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336717696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343271360))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343271936)))]; |
| tensor<fp16, [1, 77, 1280]> linear_65_cast_fp16 = 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_133_cast_fp16)[name = tensor<string, []>("linear_65_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_135_cast_fp16 = add(x = input_127_cast_fp16, y = linear_65_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_67_axes_0 = const()[name = tensor<string, []>("hidden_states_67_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343274560)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343277184)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_67_cast_fp16 = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("hidden_states_67_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343279808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344918272))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344918848)))]; |
| tensor<fp16, [1, 77, 1280]> linear_66_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_66_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344921472))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346559936))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346560512)))]; |
| tensor<fp16, [1, 77, 1280]> linear_67_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_67_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346563136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348201600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348202176)))]; |
| tensor<fp16, [1, 77, 1280]> linear_68_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_68_cast_fp16")]; |
| tensor<int32, [4]> var_760 = const()[name = tensor<string, []>("op_760"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_761_cast_fp16 = reshape(shape = var_760, x = linear_66_cast_fp16)[name = tensor<string, []>("op_761_cast_fp16")]; |
| tensor<int32, [4]> var_763 = const()[name = tensor<string, []>("op_763"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_764_cast_fp16 = reshape(shape = var_763, x = linear_67_cast_fp16)[name = tensor<string, []>("op_764_cast_fp16")]; |
| tensor<int32, [4]> var_766 = const()[name = tensor<string, []>("op_766"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_767_cast_fp16 = reshape(shape = var_766, x = linear_68_cast_fp16)[name = tensor<string, []>("op_767_cast_fp16")]; |
| tensor<int32, [4]> value_states_47_perm_0 = const()[name = tensor<string, []>("value_states_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_11_cast_fp16 = mul(x = var_761_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_11_cast_fp16")]; |
| tensor<bool, []> matmul_11_transpose_y_0 = const()[name = tensor<string, []>("matmul_11_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_11_transpose_x_0 = const()[name = tensor<string, []>("matmul_11_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_150_perm_0 = const()[name = tensor<string, []>("transpose_150_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_151_perm_0 = const()[name = tensor<string, []>("transpose_151_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_151 = transpose(perm = transpose_151_perm_0, x = var_764_cast_fp16)[name = tensor<string, []>("transpose_274")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_150 = transpose(perm = transpose_150_perm_0, x = mul_11_cast_fp16)[name = tensor<string, []>("transpose_275")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_11_cast_fp16 = matmul(transpose_x = matmul_11_transpose_x_0, transpose_y = matmul_11_transpose_y_0, x = transpose_150, y = transpose_151)[name = tensor<string, []>("matmul_11_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_11_cast_fp16 = add(x = matmul_11_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_11_cast_fp16")]; |
| tensor<int32, []> softmax_11_axis_0 = const()[name = tensor<string, []>("softmax_11_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_11_cast_fp16 = softmax(axis = softmax_11_axis_0, x = add_11_cast_fp16)[name = tensor<string, []>("softmax_11_cast_fp16")]; |
| tensor<bool, []> attn_output_45_transpose_x_0 = const()[name = tensor<string, []>("attn_output_45_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_45_transpose_y_0 = const()[name = tensor<string, []>("attn_output_45_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_47_cast_fp16 = transpose(perm = value_states_47_perm_0, x = var_767_cast_fp16)[name = tensor<string, []>("transpose_273")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_45_cast_fp16 = matmul(transpose_x = attn_output_45_transpose_x_0, transpose_y = attn_output_45_transpose_y_0, x = softmax_11_cast_fp16, y = value_states_47_cast_fp16)[name = tensor<string, []>("attn_output_45_cast_fp16")]; |
| 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_771 = const()[name = tensor<string, []>("op_771"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_47_cast_fp16 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast_fp16)[name = tensor<string, []>("transpose_272")]; |
| tensor<fp16, [1, 77, 1280]> input_137_cast_fp16 = reshape(shape = var_771, x = attn_output_47_cast_fp16)[name = tensor<string, []>("input_137_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348204800))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349843264))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349843840)))]; |
| tensor<fp16, [1, 77, 1280]> linear_69_cast_fp16 = 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_137_cast_fp16)[name = tensor<string, []>("linear_69_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_139_cast_fp16 = add(x = input_135_cast_fp16, y = linear_69_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")]; |
| tensor<int32, [1]> input_141_axes_0 = const()[name = tensor<string, []>("input_141_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349846464)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349849088)))]; |
| tensor<fp16, [1, 77, 1280]> input_141_cast_fp16 = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349851712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356405376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356405952)))]; |
| tensor<fp16, [1, 77, 5120]> linear_70_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = tensor<string, []>("linear_70_cast_fp16")]; |
| tensor<string, []> input_145_mode_0 = const()[name = tensor<string, []>("input_145_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_145_cast_fp16 = gelu(mode = input_145_mode_0, x = linear_70_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356416256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362969920))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362970496)))]; |
| tensor<fp16, [1, 77, 1280]> linear_71_cast_fp16 = 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_145_cast_fp16)[name = tensor<string, []>("linear_71_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_147_cast_fp16 = add(x = input_139_cast_fp16, y = linear_71_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_73_axes_0 = const()[name = tensor<string, []>("hidden_states_73_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362973120)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362975744)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_73_cast_fp16 = layer_norm(axes = hidden_states_73_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16, x = input_147_cast_fp16)[name = tensor<string, []>("hidden_states_73_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362978368))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364616832))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364617408)))]; |
| tensor<fp16, [1, 77, 1280]> linear_72_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_72_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364620032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366258496))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366259072)))]; |
| tensor<fp16, [1, 77, 1280]> linear_73_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_73_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366261696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367900160))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367900736)))]; |
| tensor<fp16, [1, 77, 1280]> linear_74_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_74_cast_fp16")]; |
| tensor<int32, [4]> var_815 = const()[name = tensor<string, []>("op_815"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_816_cast_fp16 = reshape(shape = var_815, x = linear_72_cast_fp16)[name = tensor<string, []>("op_816_cast_fp16")]; |
| tensor<int32, [4]> var_818 = const()[name = tensor<string, []>("op_818"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_819_cast_fp16 = reshape(shape = var_818, x = linear_73_cast_fp16)[name = tensor<string, []>("op_819_cast_fp16")]; |
| tensor<int32, [4]> var_821 = const()[name = tensor<string, []>("op_821"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_822_cast_fp16 = reshape(shape = var_821, x = linear_74_cast_fp16)[name = tensor<string, []>("op_822_cast_fp16")]; |
| tensor<int32, [4]> value_states_51_perm_0 = const()[name = tensor<string, []>("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_12_cast_fp16 = mul(x = var_816_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_12_cast_fp16")]; |
| tensor<bool, []> matmul_12_transpose_y_0 = const()[name = tensor<string, []>("matmul_12_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_12_transpose_x_0 = const()[name = tensor<string, []>("matmul_12_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_152_perm_0 = const()[name = tensor<string, []>("transpose_152_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_153_perm_0 = const()[name = tensor<string, []>("transpose_153_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_153 = transpose(perm = transpose_153_perm_0, x = var_819_cast_fp16)[name = tensor<string, []>("transpose_270")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_152 = transpose(perm = transpose_152_perm_0, x = mul_12_cast_fp16)[name = tensor<string, []>("transpose_271")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_12_cast_fp16 = matmul(transpose_x = matmul_12_transpose_x_0, transpose_y = matmul_12_transpose_y_0, x = transpose_152, y = transpose_153)[name = tensor<string, []>("matmul_12_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_12_cast_fp16 = add(x = matmul_12_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_12_cast_fp16")]; |
| tensor<int32, []> softmax_12_axis_0 = const()[name = tensor<string, []>("softmax_12_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_12_cast_fp16 = softmax(axis = softmax_12_axis_0, x = add_12_cast_fp16)[name = tensor<string, []>("softmax_12_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_822_cast_fp16)[name = tensor<string, []>("transpose_269")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = softmax_12_cast_fp16, y = value_states_51_cast_fp16)[name = tensor<string, []>("attn_output_49_cast_fp16")]; |
| tensor<int32, [4]> attn_output_51_perm_0 = const()[name = tensor<string, []>("attn_output_51_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_826 = const()[name = tensor<string, []>("op_826"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_51_cast_fp16 = transpose(perm = attn_output_51_perm_0, x = attn_output_49_cast_fp16)[name = tensor<string, []>("transpose_268")]; |
| tensor<fp16, [1, 77, 1280]> input_149_cast_fp16 = reshape(shape = var_826, x = attn_output_51_cast_fp16)[name = tensor<string, []>("input_149_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367903360))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369541824))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369542400)))]; |
| tensor<fp16, [1, 77, 1280]> linear_75_cast_fp16 = 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_149_cast_fp16)[name = tensor<string, []>("linear_75_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_151_cast_fp16 = add(x = input_147_cast_fp16, y = linear_75_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")]; |
| tensor<int32, [1]> input_153_axes_0 = const()[name = tensor<string, []>("input_153_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369545024)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369547648)))]; |
| tensor<fp16, [1, 77, 1280]> input_153_cast_fp16 = layer_norm(axes = input_153_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("input_153_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369550272))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376103936))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376104512)))]; |
| tensor<fp16, [1, 77, 5120]> linear_76_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = tensor<string, []>("linear_76_cast_fp16")]; |
| tensor<string, []> input_157_mode_0 = const()[name = tensor<string, []>("input_157_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_157_cast_fp16 = gelu(mode = input_157_mode_0, x = linear_76_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376114816))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382668480))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382669056)))]; |
| tensor<fp16, [1, 77, 1280]> linear_77_cast_fp16 = 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_157_cast_fp16)[name = tensor<string, []>("linear_77_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_159_cast_fp16 = add(x = input_151_cast_fp16, y = linear_77_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_79_axes_0 = const()[name = tensor<string, []>("hidden_states_79_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382671680)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382674304)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_79_cast_fp16 = layer_norm(axes = hidden_states_79_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16, x = input_159_cast_fp16)[name = tensor<string, []>("hidden_states_79_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382676928))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384315392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384315968)))]; |
| tensor<fp16, [1, 77, 1280]> linear_78_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_78_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384318592))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385957056))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385957632)))]; |
| tensor<fp16, [1, 77, 1280]> linear_79_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_79_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385960256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387598720))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387599296)))]; |
| tensor<fp16, [1, 77, 1280]> linear_80_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_80_cast_fp16")]; |
| tensor<int32, [4]> var_870 = const()[name = tensor<string, []>("op_870"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_871_cast_fp16 = reshape(shape = var_870, x = linear_78_cast_fp16)[name = tensor<string, []>("op_871_cast_fp16")]; |
| tensor<int32, [4]> var_873 = const()[name = tensor<string, []>("op_873"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_874_cast_fp16 = reshape(shape = var_873, x = linear_79_cast_fp16)[name = tensor<string, []>("op_874_cast_fp16")]; |
| tensor<int32, [4]> var_876 = const()[name = tensor<string, []>("op_876"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_877_cast_fp16 = reshape(shape = var_876, x = linear_80_cast_fp16)[name = tensor<string, []>("op_877_cast_fp16")]; |
| tensor<int32, [4]> value_states_55_perm_0 = const()[name = tensor<string, []>("value_states_55_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_13_cast_fp16 = mul(x = var_871_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_13_cast_fp16")]; |
| tensor<bool, []> matmul_13_transpose_y_0 = const()[name = tensor<string, []>("matmul_13_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_13_transpose_x_0 = const()[name = tensor<string, []>("matmul_13_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_154_perm_0 = const()[name = tensor<string, []>("transpose_154_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_155_perm_0 = const()[name = tensor<string, []>("transpose_155_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_155 = transpose(perm = transpose_155_perm_0, x = var_874_cast_fp16)[name = tensor<string, []>("transpose_266")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_154 = transpose(perm = transpose_154_perm_0, x = mul_13_cast_fp16)[name = tensor<string, []>("transpose_267")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_13_cast_fp16 = matmul(transpose_x = matmul_13_transpose_x_0, transpose_y = matmul_13_transpose_y_0, x = transpose_154, y = transpose_155)[name = tensor<string, []>("matmul_13_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_13_cast_fp16 = add(x = matmul_13_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_13_cast_fp16")]; |
| tensor<int32, []> softmax_13_axis_0 = const()[name = tensor<string, []>("softmax_13_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_13_cast_fp16 = softmax(axis = softmax_13_axis_0, x = add_13_cast_fp16)[name = tensor<string, []>("softmax_13_cast_fp16")]; |
| tensor<bool, []> attn_output_53_transpose_x_0 = const()[name = tensor<string, []>("attn_output_53_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_53_transpose_y_0 = const()[name = tensor<string, []>("attn_output_53_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_55_cast_fp16 = transpose(perm = value_states_55_perm_0, x = var_877_cast_fp16)[name = tensor<string, []>("transpose_265")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_53_cast_fp16 = matmul(transpose_x = attn_output_53_transpose_x_0, transpose_y = attn_output_53_transpose_y_0, x = softmax_13_cast_fp16, y = value_states_55_cast_fp16)[name = tensor<string, []>("attn_output_53_cast_fp16")]; |
| tensor<int32, [4]> attn_output_55_perm_0 = const()[name = tensor<string, []>("attn_output_55_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_881 = const()[name = tensor<string, []>("op_881"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_55_cast_fp16 = transpose(perm = attn_output_55_perm_0, x = attn_output_53_cast_fp16)[name = tensor<string, []>("transpose_264")]; |
| tensor<fp16, [1, 77, 1280]> input_161_cast_fp16 = reshape(shape = var_881, x = attn_output_55_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387601920))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389240384))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389240960)))]; |
| tensor<fp16, [1, 77, 1280]> linear_81_cast_fp16 = 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_161_cast_fp16)[name = tensor<string, []>("linear_81_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_163_cast_fp16 = add(x = input_159_cast_fp16, y = linear_81_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")]; |
| tensor<int32, [1]> input_165_axes_0 = const()[name = tensor<string, []>("input_165_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389243584)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389246208)))]; |
| tensor<fp16, [1, 77, 1280]> input_165_cast_fp16 = layer_norm(axes = input_165_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16, x = input_163_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389248832))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395802496))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395803072)))]; |
| tensor<fp16, [1, 77, 5120]> linear_82_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = tensor<string, []>("linear_82_cast_fp16")]; |
| tensor<string, []> input_169_mode_0 = const()[name = tensor<string, []>("input_169_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_82_cast_fp16)[name = tensor<string, []>("input_169_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395813376))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402367040))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402367616)))]; |
| tensor<fp16, [1, 77, 1280]> linear_83_cast_fp16 = 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_169_cast_fp16)[name = tensor<string, []>("linear_83_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_171_cast_fp16 = add(x = input_163_cast_fp16, y = linear_83_cast_fp16)[name = tensor<string, []>("input_171_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_85_axes_0 = const()[name = tensor<string, []>("hidden_states_85_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402370240)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402372864)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_85_cast_fp16 = layer_norm(axes = hidden_states_85_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16, x = input_171_cast_fp16)[name = tensor<string, []>("hidden_states_85_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402375488))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404013952))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404014528)))]; |
| tensor<fp16, [1, 77, 1280]> linear_84_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_84_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404017152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405655616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405656192)))]; |
| tensor<fp16, [1, 77, 1280]> linear_85_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_85_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405658816))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407297280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407297856)))]; |
| tensor<fp16, [1, 77, 1280]> linear_86_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_86_cast_fp16")]; |
| tensor<int32, [4]> var_925 = const()[name = tensor<string, []>("op_925"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_926_cast_fp16 = reshape(shape = var_925, x = linear_84_cast_fp16)[name = tensor<string, []>("op_926_cast_fp16")]; |
| tensor<int32, [4]> var_928 = const()[name = tensor<string, []>("op_928"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_929_cast_fp16 = reshape(shape = var_928, x = linear_85_cast_fp16)[name = tensor<string, []>("op_929_cast_fp16")]; |
| tensor<int32, [4]> var_931 = const()[name = tensor<string, []>("op_931"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_932_cast_fp16 = reshape(shape = var_931, x = linear_86_cast_fp16)[name = tensor<string, []>("op_932_cast_fp16")]; |
| tensor<int32, [4]> value_states_59_perm_0 = const()[name = tensor<string, []>("value_states_59_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_14_cast_fp16 = mul(x = var_926_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_14_cast_fp16")]; |
| tensor<bool, []> matmul_14_transpose_y_0 = const()[name = tensor<string, []>("matmul_14_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_14_transpose_x_0 = const()[name = tensor<string, []>("matmul_14_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_156_perm_0 = const()[name = tensor<string, []>("transpose_156_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_157_perm_0 = const()[name = tensor<string, []>("transpose_157_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_157 = transpose(perm = transpose_157_perm_0, x = var_929_cast_fp16)[name = tensor<string, []>("transpose_262")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_156 = transpose(perm = transpose_156_perm_0, x = mul_14_cast_fp16)[name = tensor<string, []>("transpose_263")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_14_cast_fp16 = matmul(transpose_x = matmul_14_transpose_x_0, transpose_y = matmul_14_transpose_y_0, x = transpose_156, y = transpose_157)[name = tensor<string, []>("matmul_14_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_14_cast_fp16 = add(x = matmul_14_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_14_cast_fp16")]; |
| tensor<int32, []> softmax_14_axis_0 = const()[name = tensor<string, []>("softmax_14_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_14_cast_fp16 = softmax(axis = softmax_14_axis_0, x = add_14_cast_fp16)[name = tensor<string, []>("softmax_14_cast_fp16")]; |
| tensor<bool, []> attn_output_57_transpose_x_0 = const()[name = tensor<string, []>("attn_output_57_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_57_transpose_y_0 = const()[name = tensor<string, []>("attn_output_57_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_59_cast_fp16 = transpose(perm = value_states_59_perm_0, x = var_932_cast_fp16)[name = tensor<string, []>("transpose_261")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_0, transpose_y = attn_output_57_transpose_y_0, x = softmax_14_cast_fp16, y = value_states_59_cast_fp16)[name = tensor<string, []>("attn_output_57_cast_fp16")]; |
| 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_936 = const()[name = tensor<string, []>("op_936"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_59_cast_fp16 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast_fp16)[name = tensor<string, []>("transpose_260")]; |
| tensor<fp16, [1, 77, 1280]> input_173_cast_fp16 = reshape(shape = var_936, x = attn_output_59_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407300480))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408938944))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408939520)))]; |
| tensor<fp16, [1, 77, 1280]> linear_87_cast_fp16 = 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_173_cast_fp16)[name = tensor<string, []>("linear_87_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_175_cast_fp16 = add(x = input_171_cast_fp16, y = linear_87_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")]; |
| tensor<int32, [1]> input_177_axes_0 = const()[name = tensor<string, []>("input_177_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408942144)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408944768)))]; |
| tensor<fp16, [1, 77, 1280]> input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408947392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415501056))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415501632)))]; |
| tensor<fp16, [1, 77, 5120]> linear_88_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor<string, []>("linear_88_cast_fp16")]; |
| tensor<string, []> input_181_mode_0 = const()[name = tensor<string, []>("input_181_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_181_cast_fp16 = gelu(mode = input_181_mode_0, x = linear_88_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415511936))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422065600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422066176)))]; |
| tensor<fp16, [1, 77, 1280]> linear_89_cast_fp16 = 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_181_cast_fp16)[name = tensor<string, []>("linear_89_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_183_cast_fp16 = add(x = input_175_cast_fp16, y = linear_89_cast_fp16)[name = tensor<string, []>("input_183_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_91_axes_0 = const()[name = tensor<string, []>("hidden_states_91_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422068800)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422071424)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_91_cast_fp16 = layer_norm(axes = hidden_states_91_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16, x = input_183_cast_fp16)[name = tensor<string, []>("hidden_states_91_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422074048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423712512))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423713088)))]; |
| tensor<fp16, [1, 77, 1280]> linear_90_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_90_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423715712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425354176))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425354752)))]; |
| tensor<fp16, [1, 77, 1280]> linear_91_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_91_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425357376))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426995840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426996416)))]; |
| tensor<fp16, [1, 77, 1280]> linear_92_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_92_cast_fp16")]; |
| tensor<int32, [4]> var_980 = const()[name = tensor<string, []>("op_980"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_981_cast_fp16 = reshape(shape = var_980, x = linear_90_cast_fp16)[name = tensor<string, []>("op_981_cast_fp16")]; |
| tensor<int32, [4]> var_983 = const()[name = tensor<string, []>("op_983"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_984_cast_fp16 = reshape(shape = var_983, x = linear_91_cast_fp16)[name = tensor<string, []>("op_984_cast_fp16")]; |
| tensor<int32, [4]> var_986 = const()[name = tensor<string, []>("op_986"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_987_cast_fp16 = reshape(shape = var_986, x = linear_92_cast_fp16)[name = tensor<string, []>("op_987_cast_fp16")]; |
| tensor<int32, [4]> value_states_63_perm_0 = const()[name = tensor<string, []>("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_15_cast_fp16 = mul(x = var_981_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_15_cast_fp16")]; |
| tensor<bool, []> matmul_15_transpose_y_0 = const()[name = tensor<string, []>("matmul_15_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_15_transpose_x_0 = const()[name = tensor<string, []>("matmul_15_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_158_perm_0 = const()[name = tensor<string, []>("transpose_158_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_159_perm_0 = const()[name = tensor<string, []>("transpose_159_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_159 = transpose(perm = transpose_159_perm_0, x = var_984_cast_fp16)[name = tensor<string, []>("transpose_258")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_158 = transpose(perm = transpose_158_perm_0, x = mul_15_cast_fp16)[name = tensor<string, []>("transpose_259")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_15_cast_fp16 = matmul(transpose_x = matmul_15_transpose_x_0, transpose_y = matmul_15_transpose_y_0, x = transpose_158, y = transpose_159)[name = tensor<string, []>("matmul_15_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_15_cast_fp16 = add(x = matmul_15_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_15_cast_fp16")]; |
| tensor<int32, []> softmax_15_axis_0 = const()[name = tensor<string, []>("softmax_15_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_15_cast_fp16 = softmax(axis = softmax_15_axis_0, x = add_15_cast_fp16)[name = tensor<string, []>("softmax_15_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_987_cast_fp16)[name = tensor<string, []>("transpose_257")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_61_cast_fp16 = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = softmax_15_cast_fp16, y = value_states_63_cast_fp16)[name = tensor<string, []>("attn_output_61_cast_fp16")]; |
| tensor<int32, [4]> attn_output_63_perm_0 = const()[name = tensor<string, []>("attn_output_63_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_63_cast_fp16 = transpose(perm = attn_output_63_perm_0, x = attn_output_61_cast_fp16)[name = tensor<string, []>("transpose_256")]; |
| tensor<fp16, [1, 77, 1280]> input_185_cast_fp16 = reshape(shape = var_991, x = attn_output_63_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426999040))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428637504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428638080)))]; |
| tensor<fp16, [1, 77, 1280]> linear_93_cast_fp16 = 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_185_cast_fp16)[name = tensor<string, []>("linear_93_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_187_cast_fp16 = add(x = input_183_cast_fp16, y = linear_93_cast_fp16)[name = tensor<string, []>("input_187_cast_fp16")]; |
| tensor<int32, [1]> input_189_axes_0 = const()[name = tensor<string, []>("input_189_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428640704)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428643328)))]; |
| tensor<fp16, [1, 77, 1280]> input_189_cast_fp16 = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16, x = input_187_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428645952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435199616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435200192)))]; |
| tensor<fp16, [1, 77, 5120]> linear_94_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = tensor<string, []>("linear_94_cast_fp16")]; |
| tensor<string, []> input_193_mode_0 = const()[name = tensor<string, []>("input_193_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_94_cast_fp16)[name = tensor<string, []>("input_193_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435210496))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441764160))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441764736)))]; |
| tensor<fp16, [1, 77, 1280]> linear_95_cast_fp16 = 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_193_cast_fp16)[name = tensor<string, []>("linear_95_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_95_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_97_axes_0 = const()[name = tensor<string, []>("hidden_states_97_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441767360)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441769984)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_97_cast_fp16 = layer_norm(axes = hidden_states_97_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16, x = input_195_cast_fp16)[name = tensor<string, []>("hidden_states_97_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441772608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443411072))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443411648)))]; |
| tensor<fp16, [1, 77, 1280]> linear_96_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_96_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443414272))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445052736))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445053312)))]; |
| tensor<fp16, [1, 77, 1280]> linear_97_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_97_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445055936))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446694400))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446694976)))]; |
| tensor<fp16, [1, 77, 1280]> linear_98_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_98_cast_fp16")]; |
| tensor<int32, [4]> var_1035 = const()[name = tensor<string, []>("op_1035"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1036_cast_fp16 = reshape(shape = var_1035, x = linear_96_cast_fp16)[name = tensor<string, []>("op_1036_cast_fp16")]; |
| tensor<int32, [4]> var_1038 = const()[name = tensor<string, []>("op_1038"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1039_cast_fp16 = reshape(shape = var_1038, x = linear_97_cast_fp16)[name = tensor<string, []>("op_1039_cast_fp16")]; |
| tensor<int32, [4]> var_1041 = const()[name = tensor<string, []>("op_1041"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1042_cast_fp16 = reshape(shape = var_1041, x = linear_98_cast_fp16)[name = tensor<string, []>("op_1042_cast_fp16")]; |
| tensor<int32, [4]> value_states_67_perm_0 = const()[name = tensor<string, []>("value_states_67_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_16_cast_fp16 = mul(x = var_1036_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_16_cast_fp16")]; |
| tensor<bool, []> matmul_16_transpose_y_0 = const()[name = tensor<string, []>("matmul_16_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_16_transpose_x_0 = const()[name = tensor<string, []>("matmul_16_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_160_perm_0 = const()[name = tensor<string, []>("transpose_160_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_161_perm_0 = const()[name = tensor<string, []>("transpose_161_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_161 = transpose(perm = transpose_161_perm_0, x = var_1039_cast_fp16)[name = tensor<string, []>("transpose_254")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_160 = transpose(perm = transpose_160_perm_0, x = mul_16_cast_fp16)[name = tensor<string, []>("transpose_255")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_16_cast_fp16 = matmul(transpose_x = matmul_16_transpose_x_0, transpose_y = matmul_16_transpose_y_0, x = transpose_160, y = transpose_161)[name = tensor<string, []>("matmul_16_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_16_cast_fp16 = add(x = matmul_16_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_16_cast_fp16")]; |
| tensor<int32, []> softmax_16_axis_0 = const()[name = tensor<string, []>("softmax_16_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_16_cast_fp16 = softmax(axis = softmax_16_axis_0, x = add_16_cast_fp16)[name = tensor<string, []>("softmax_16_cast_fp16")]; |
| tensor<bool, []> attn_output_65_transpose_x_0 = const()[name = tensor<string, []>("attn_output_65_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_65_transpose_y_0 = const()[name = tensor<string, []>("attn_output_65_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_67_cast_fp16 = transpose(perm = value_states_67_perm_0, x = var_1042_cast_fp16)[name = tensor<string, []>("transpose_253")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_0, transpose_y = attn_output_65_transpose_y_0, x = softmax_16_cast_fp16, y = value_states_67_cast_fp16)[name = tensor<string, []>("attn_output_65_cast_fp16")]; |
| tensor<int32, [4]> attn_output_67_perm_0 = const()[name = tensor<string, []>("attn_output_67_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1046 = const()[name = tensor<string, []>("op_1046"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_67_cast_fp16 = transpose(perm = attn_output_67_perm_0, x = attn_output_65_cast_fp16)[name = tensor<string, []>("transpose_252")]; |
| tensor<fp16, [1, 77, 1280]> input_197_cast_fp16 = reshape(shape = var_1046, x = attn_output_67_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446697600))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448336064))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448336640)))]; |
| tensor<fp16, [1, 77, 1280]> linear_99_cast_fp16 = 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_197_cast_fp16)[name = tensor<string, []>("linear_99_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_199_cast_fp16 = add(x = input_195_cast_fp16, y = linear_99_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")]; |
| tensor<int32, [1]> input_201_axes_0 = const()[name = tensor<string, []>("input_201_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448339264)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448341888)))]; |
| tensor<fp16, [1, 77, 1280]> input_201_cast_fp16 = layer_norm(axes = input_201_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16, x = input_199_cast_fp16)[name = tensor<string, []>("input_201_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448344512))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454898176))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454898752)))]; |
| tensor<fp16, [1, 77, 5120]> linear_100_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = tensor<string, []>("linear_100_cast_fp16")]; |
| tensor<string, []> input_205_mode_0 = const()[name = tensor<string, []>("input_205_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_205_cast_fp16 = gelu(mode = input_205_mode_0, x = linear_100_cast_fp16)[name = tensor<string, []>("input_205_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454909056))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461462720))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461463296)))]; |
| tensor<fp16, [1, 77, 1280]> linear_101_cast_fp16 = 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_205_cast_fp16)[name = tensor<string, []>("linear_101_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_207_cast_fp16 = add(x = input_199_cast_fp16, y = linear_101_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_103_axes_0 = const()[name = tensor<string, []>("hidden_states_103_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461465920)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461468544)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_103_cast_fp16 = layer_norm(axes = hidden_states_103_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16, x = input_207_cast_fp16)[name = tensor<string, []>("hidden_states_103_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461471168))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463109632))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463110208)))]; |
| tensor<fp16, [1, 77, 1280]> linear_102_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_102_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463112832))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464751296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464751872)))]; |
| tensor<fp16, [1, 77, 1280]> linear_103_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_103_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464754496))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466392960))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466393536)))]; |
| tensor<fp16, [1, 77, 1280]> linear_104_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_104_cast_fp16")]; |
| tensor<int32, [4]> var_1090 = const()[name = tensor<string, []>("op_1090"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1091_cast_fp16 = reshape(shape = var_1090, x = linear_102_cast_fp16)[name = tensor<string, []>("op_1091_cast_fp16")]; |
| tensor<int32, [4]> var_1093 = const()[name = tensor<string, []>("op_1093"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1094_cast_fp16 = reshape(shape = var_1093, x = linear_103_cast_fp16)[name = tensor<string, []>("op_1094_cast_fp16")]; |
| tensor<int32, [4]> var_1096 = const()[name = tensor<string, []>("op_1096"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1097_cast_fp16 = reshape(shape = var_1096, x = linear_104_cast_fp16)[name = tensor<string, []>("op_1097_cast_fp16")]; |
| tensor<int32, [4]> value_states_71_perm_0 = const()[name = tensor<string, []>("value_states_71_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_17_cast_fp16 = mul(x = var_1091_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_17_cast_fp16")]; |
| tensor<bool, []> matmul_17_transpose_y_0 = const()[name = tensor<string, []>("matmul_17_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_17_transpose_x_0 = const()[name = tensor<string, []>("matmul_17_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_162_perm_0 = const()[name = tensor<string, []>("transpose_162_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_163_perm_0 = const()[name = tensor<string, []>("transpose_163_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_163 = transpose(perm = transpose_163_perm_0, x = var_1094_cast_fp16)[name = tensor<string, []>("transpose_250")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_162 = transpose(perm = transpose_162_perm_0, x = mul_17_cast_fp16)[name = tensor<string, []>("transpose_251")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_17_cast_fp16 = matmul(transpose_x = matmul_17_transpose_x_0, transpose_y = matmul_17_transpose_y_0, x = transpose_162, y = transpose_163)[name = tensor<string, []>("matmul_17_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_17_cast_fp16 = add(x = matmul_17_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_17_cast_fp16")]; |
| tensor<int32, []> softmax_17_axis_0 = const()[name = tensor<string, []>("softmax_17_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_17_cast_fp16 = softmax(axis = softmax_17_axis_0, x = add_17_cast_fp16)[name = tensor<string, []>("softmax_17_cast_fp16")]; |
| tensor<bool, []> attn_output_69_transpose_x_0 = const()[name = tensor<string, []>("attn_output_69_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_69_transpose_y_0 = const()[name = tensor<string, []>("attn_output_69_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_71_cast_fp16 = transpose(perm = value_states_71_perm_0, x = var_1097_cast_fp16)[name = tensor<string, []>("transpose_249")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_69_cast_fp16 = matmul(transpose_x = attn_output_69_transpose_x_0, transpose_y = attn_output_69_transpose_y_0, x = softmax_17_cast_fp16, y = value_states_71_cast_fp16)[name = tensor<string, []>("attn_output_69_cast_fp16")]; |
| 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_1101 = const()[name = tensor<string, []>("op_1101"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_71_cast_fp16 = transpose(perm = attn_output_71_perm_0, x = attn_output_69_cast_fp16)[name = tensor<string, []>("transpose_248")]; |
| tensor<fp16, [1, 77, 1280]> input_209_cast_fp16 = reshape(shape = var_1101, x = attn_output_71_cast_fp16)[name = tensor<string, []>("input_209_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466396160))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468034624))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468035200)))]; |
| tensor<fp16, [1, 77, 1280]> linear_105_cast_fp16 = 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_209_cast_fp16)[name = tensor<string, []>("linear_105_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_211_cast_fp16 = add(x = input_207_cast_fp16, y = linear_105_cast_fp16)[name = tensor<string, []>("input_211_cast_fp16")]; |
| tensor<int32, [1]> input_213_axes_0 = const()[name = tensor<string, []>("input_213_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468037824)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468040448)))]; |
| tensor<fp16, [1, 77, 1280]> input_213_cast_fp16 = layer_norm(axes = input_213_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16, x = input_211_cast_fp16)[name = tensor<string, []>("input_213_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468043072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474596736))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474597312)))]; |
| tensor<fp16, [1, 77, 5120]> linear_106_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = tensor<string, []>("linear_106_cast_fp16")]; |
| tensor<string, []> input_217_mode_0 = const()[name = tensor<string, []>("input_217_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_217_cast_fp16 = gelu(mode = input_217_mode_0, x = linear_106_cast_fp16)[name = tensor<string, []>("input_217_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474607616))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481161280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481161856)))]; |
| tensor<fp16, [1, 77, 1280]> linear_107_cast_fp16 = 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_217_cast_fp16)[name = tensor<string, []>("linear_107_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_219_cast_fp16 = add(x = input_211_cast_fp16, y = linear_107_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_109_axes_0 = const()[name = tensor<string, []>("hidden_states_109_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481164480)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481167104)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_109_cast_fp16 = layer_norm(axes = hidden_states_109_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("hidden_states_109_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481169728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482808192))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482808768)))]; |
| tensor<fp16, [1, 77, 1280]> linear_108_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_108_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482811392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484449856))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484450432)))]; |
| tensor<fp16, [1, 77, 1280]> linear_109_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_109_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484453056))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486091520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486092096)))]; |
| tensor<fp16, [1, 77, 1280]> linear_110_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_110_cast_fp16")]; |
| tensor<int32, [4]> var_1145 = const()[name = tensor<string, []>("op_1145"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1146_cast_fp16 = reshape(shape = var_1145, x = linear_108_cast_fp16)[name = tensor<string, []>("op_1146_cast_fp16")]; |
| tensor<int32, [4]> var_1148 = const()[name = tensor<string, []>("op_1148"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1149_cast_fp16 = reshape(shape = var_1148, x = linear_109_cast_fp16)[name = tensor<string, []>("op_1149_cast_fp16")]; |
| tensor<int32, [4]> var_1151 = const()[name = tensor<string, []>("op_1151"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1152_cast_fp16 = reshape(shape = var_1151, x = linear_110_cast_fp16)[name = tensor<string, []>("op_1152_cast_fp16")]; |
| tensor<int32, [4]> value_states_75_perm_0 = const()[name = tensor<string, []>("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_18_cast_fp16 = mul(x = var_1146_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_18_cast_fp16")]; |
| tensor<bool, []> matmul_18_transpose_y_0 = const()[name = tensor<string, []>("matmul_18_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_18_transpose_x_0 = const()[name = tensor<string, []>("matmul_18_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_164_perm_0 = const()[name = tensor<string, []>("transpose_164_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_165_perm_0 = const()[name = tensor<string, []>("transpose_165_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_165 = transpose(perm = transpose_165_perm_0, x = var_1149_cast_fp16)[name = tensor<string, []>("transpose_246")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_164 = transpose(perm = transpose_164_perm_0, x = mul_18_cast_fp16)[name = tensor<string, []>("transpose_247")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_18_cast_fp16 = matmul(transpose_x = matmul_18_transpose_x_0, transpose_y = matmul_18_transpose_y_0, x = transpose_164, y = transpose_165)[name = tensor<string, []>("matmul_18_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_18_cast_fp16 = add(x = matmul_18_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_18_cast_fp16")]; |
| tensor<int32, []> softmax_18_axis_0 = const()[name = tensor<string, []>("softmax_18_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_18_cast_fp16 = softmax(axis = softmax_18_axis_0, x = add_18_cast_fp16)[name = tensor<string, []>("softmax_18_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_1152_cast_fp16)[name = tensor<string, []>("transpose_245")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_0, transpose_y = attn_output_73_transpose_y_0, x = softmax_18_cast_fp16, y = value_states_75_cast_fp16)[name = tensor<string, []>("attn_output_73_cast_fp16")]; |
| tensor<int32, [4]> attn_output_75_perm_0 = const()[name = tensor<string, []>("attn_output_75_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1156 = const()[name = tensor<string, []>("op_1156"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_75_cast_fp16 = transpose(perm = attn_output_75_perm_0, x = attn_output_73_cast_fp16)[name = tensor<string, []>("transpose_244")]; |
| tensor<fp16, [1, 77, 1280]> input_221_cast_fp16 = reshape(shape = var_1156, x = attn_output_75_cast_fp16)[name = tensor<string, []>("input_221_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486094720))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487733184))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487733760)))]; |
| tensor<fp16, [1, 77, 1280]> linear_111_cast_fp16 = 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_221_cast_fp16)[name = tensor<string, []>("linear_111_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_223_cast_fp16 = add(x = input_219_cast_fp16, y = linear_111_cast_fp16)[name = tensor<string, []>("input_223_cast_fp16")]; |
| tensor<int32, [1]> input_225_axes_0 = const()[name = tensor<string, []>("input_225_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487736384)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487739008)))]; |
| tensor<fp16, [1, 77, 1280]> input_225_cast_fp16 = layer_norm(axes = input_225_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16, x = input_223_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487741632))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494295296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494295872)))]; |
| tensor<fp16, [1, 77, 5120]> linear_112_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = tensor<string, []>("linear_112_cast_fp16")]; |
| tensor<string, []> input_229_mode_0 = const()[name = tensor<string, []>("input_229_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_229_cast_fp16 = gelu(mode = input_229_mode_0, x = linear_112_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494306176))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500859840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500860416)))]; |
| tensor<fp16, [1, 77, 1280]> linear_113_cast_fp16 = 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_229_cast_fp16)[name = tensor<string, []>("linear_113_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_231_cast_fp16 = add(x = input_223_cast_fp16, y = linear_113_cast_fp16)[name = tensor<string, []>("input_231_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_115_axes_0 = const()[name = tensor<string, []>("hidden_states_115_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500863040)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500865664)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_115_cast_fp16 = layer_norm(axes = hidden_states_115_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16, x = input_231_cast_fp16)[name = tensor<string, []>("hidden_states_115_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500868288))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502506752))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502507328)))]; |
| tensor<fp16, [1, 77, 1280]> linear_114_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_114_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502509952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504148416))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504148992)))]; |
| tensor<fp16, [1, 77, 1280]> linear_115_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_115_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504151616))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505790080))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505790656)))]; |
| tensor<fp16, [1, 77, 1280]> linear_116_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_116_cast_fp16")]; |
| tensor<int32, [4]> var_1200 = const()[name = tensor<string, []>("op_1200"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1201_cast_fp16 = reshape(shape = var_1200, x = linear_114_cast_fp16)[name = tensor<string, []>("op_1201_cast_fp16")]; |
| tensor<int32, [4]> var_1203 = const()[name = tensor<string, []>("op_1203"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1204_cast_fp16 = reshape(shape = var_1203, x = linear_115_cast_fp16)[name = tensor<string, []>("op_1204_cast_fp16")]; |
| tensor<int32, [4]> var_1206 = const()[name = tensor<string, []>("op_1206"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1207_cast_fp16 = reshape(shape = var_1206, x = linear_116_cast_fp16)[name = tensor<string, []>("op_1207_cast_fp16")]; |
| tensor<int32, [4]> value_states_79_perm_0 = const()[name = tensor<string, []>("value_states_79_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_19_cast_fp16 = mul(x = var_1201_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_19_cast_fp16")]; |
| tensor<bool, []> matmul_19_transpose_y_0 = const()[name = tensor<string, []>("matmul_19_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_19_transpose_x_0 = const()[name = tensor<string, []>("matmul_19_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_166_perm_0 = const()[name = tensor<string, []>("transpose_166_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_167_perm_0 = const()[name = tensor<string, []>("transpose_167_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_167 = transpose(perm = transpose_167_perm_0, x = var_1204_cast_fp16)[name = tensor<string, []>("transpose_242")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_166 = transpose(perm = transpose_166_perm_0, x = mul_19_cast_fp16)[name = tensor<string, []>("transpose_243")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_19_cast_fp16 = matmul(transpose_x = matmul_19_transpose_x_0, transpose_y = matmul_19_transpose_y_0, x = transpose_166, y = transpose_167)[name = tensor<string, []>("matmul_19_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_19_cast_fp16 = add(x = matmul_19_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_19_cast_fp16")]; |
| tensor<int32, []> softmax_19_axis_0 = const()[name = tensor<string, []>("softmax_19_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_19_cast_fp16 = softmax(axis = softmax_19_axis_0, x = add_19_cast_fp16)[name = tensor<string, []>("softmax_19_cast_fp16")]; |
| tensor<bool, []> attn_output_77_transpose_x_0 = const()[name = tensor<string, []>("attn_output_77_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_77_transpose_y_0 = const()[name = tensor<string, []>("attn_output_77_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_79_cast_fp16 = transpose(perm = value_states_79_perm_0, x = var_1207_cast_fp16)[name = tensor<string, []>("transpose_241")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_77_cast_fp16 = matmul(transpose_x = attn_output_77_transpose_x_0, transpose_y = attn_output_77_transpose_y_0, x = softmax_19_cast_fp16, y = value_states_79_cast_fp16)[name = tensor<string, []>("attn_output_77_cast_fp16")]; |
| tensor<int32, [4]> attn_output_79_perm_0 = const()[name = tensor<string, []>("attn_output_79_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1211 = const()[name = tensor<string, []>("op_1211"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_79_cast_fp16 = transpose(perm = attn_output_79_perm_0, x = attn_output_77_cast_fp16)[name = tensor<string, []>("transpose_240")]; |
| tensor<fp16, [1, 77, 1280]> input_233_cast_fp16 = reshape(shape = var_1211, x = attn_output_79_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505793280))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507431744))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507432320)))]; |
| tensor<fp16, [1, 77, 1280]> linear_117_cast_fp16 = 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_233_cast_fp16)[name = tensor<string, []>("linear_117_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_235_cast_fp16 = add(x = input_231_cast_fp16, y = linear_117_cast_fp16)[name = tensor<string, []>("input_235_cast_fp16")]; |
| tensor<int32, [1]> input_237_axes_0 = const()[name = tensor<string, []>("input_237_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507434944)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507437568)))]; |
| tensor<fp16, [1, 77, 1280]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16, x = input_235_cast_fp16)[name = tensor<string, []>("input_237_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507440192))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513993856))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513994432)))]; |
| tensor<fp16, [1, 77, 5120]> linear_118_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = tensor<string, []>("linear_118_cast_fp16")]; |
| tensor<string, []> input_241_mode_0 = const()[name = tensor<string, []>("input_241_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_241_cast_fp16 = gelu(mode = input_241_mode_0, x = linear_118_cast_fp16)[name = tensor<string, []>("input_241_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514004736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520558400))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520558976)))]; |
| tensor<fp16, [1, 77, 1280]> linear_119_cast_fp16 = 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_241_cast_fp16)[name = tensor<string, []>("linear_119_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_243_cast_fp16 = add(x = input_235_cast_fp16, y = linear_119_cast_fp16)[name = tensor<string, []>("input_243_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_121_axes_0 = const()[name = tensor<string, []>("hidden_states_121_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520561600)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520564224)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_121_cast_fp16 = layer_norm(axes = hidden_states_121_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16, x = input_243_cast_fp16)[name = tensor<string, []>("hidden_states_121_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520566848))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522205312))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522205888)))]; |
| tensor<fp16, [1, 77, 1280]> linear_120_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_120_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522208512))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523846976))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523847552)))]; |
| tensor<fp16, [1, 77, 1280]> linear_121_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_121_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523850176))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525488640))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525489216)))]; |
| tensor<fp16, [1, 77, 1280]> linear_122_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_122_cast_fp16")]; |
| tensor<int32, [4]> var_1255 = const()[name = tensor<string, []>("op_1255"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1256_cast_fp16 = reshape(shape = var_1255, x = linear_120_cast_fp16)[name = tensor<string, []>("op_1256_cast_fp16")]; |
| tensor<int32, [4]> var_1258 = const()[name = tensor<string, []>("op_1258"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1259_cast_fp16 = reshape(shape = var_1258, x = linear_121_cast_fp16)[name = tensor<string, []>("op_1259_cast_fp16")]; |
| tensor<int32, [4]> var_1261 = const()[name = tensor<string, []>("op_1261"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1262_cast_fp16 = reshape(shape = var_1261, x = linear_122_cast_fp16)[name = tensor<string, []>("op_1262_cast_fp16")]; |
| tensor<int32, [4]> value_states_83_perm_0 = const()[name = tensor<string, []>("value_states_83_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_20_cast_fp16 = mul(x = var_1256_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_20_cast_fp16")]; |
| tensor<bool, []> matmul_20_transpose_y_0 = const()[name = tensor<string, []>("matmul_20_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_20_transpose_x_0 = const()[name = tensor<string, []>("matmul_20_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_168_perm_0 = const()[name = tensor<string, []>("transpose_168_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_169_perm_0 = const()[name = tensor<string, []>("transpose_169_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_169 = transpose(perm = transpose_169_perm_0, x = var_1259_cast_fp16)[name = tensor<string, []>("transpose_238")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_168 = transpose(perm = transpose_168_perm_0, x = mul_20_cast_fp16)[name = tensor<string, []>("transpose_239")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_20_cast_fp16 = matmul(transpose_x = matmul_20_transpose_x_0, transpose_y = matmul_20_transpose_y_0, x = transpose_168, y = transpose_169)[name = tensor<string, []>("matmul_20_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_20_cast_fp16 = add(x = matmul_20_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_20_cast_fp16")]; |
| tensor<int32, []> softmax_20_axis_0 = const()[name = tensor<string, []>("softmax_20_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_20_cast_fp16 = softmax(axis = softmax_20_axis_0, x = add_20_cast_fp16)[name = tensor<string, []>("softmax_20_cast_fp16")]; |
| tensor<bool, []> attn_output_81_transpose_x_0 = const()[name = tensor<string, []>("attn_output_81_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_81_transpose_y_0 = const()[name = tensor<string, []>("attn_output_81_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_83_cast_fp16 = transpose(perm = value_states_83_perm_0, x = var_1262_cast_fp16)[name = tensor<string, []>("transpose_237")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_0, transpose_y = attn_output_81_transpose_y_0, x = softmax_20_cast_fp16, y = value_states_83_cast_fp16)[name = tensor<string, []>("attn_output_81_cast_fp16")]; |
| 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_1266 = const()[name = tensor<string, []>("op_1266"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_83_cast_fp16 = transpose(perm = attn_output_83_perm_0, x = attn_output_81_cast_fp16)[name = tensor<string, []>("transpose_236")]; |
| tensor<fp16, [1, 77, 1280]> input_245_cast_fp16 = reshape(shape = var_1266, x = attn_output_83_cast_fp16)[name = tensor<string, []>("input_245_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525491840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527130304))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527130880)))]; |
| tensor<fp16, [1, 77, 1280]> linear_123_cast_fp16 = 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_245_cast_fp16)[name = tensor<string, []>("linear_123_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_247_cast_fp16 = add(x = input_243_cast_fp16, y = linear_123_cast_fp16)[name = tensor<string, []>("input_247_cast_fp16")]; |
| tensor<int32, [1]> input_249_axes_0 = const()[name = tensor<string, []>("input_249_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527133504)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527136128)))]; |
| tensor<fp16, [1, 77, 1280]> input_249_cast_fp16 = layer_norm(axes = input_249_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16, x = input_247_cast_fp16)[name = tensor<string, []>("input_249_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527138752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533692416))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533692992)))]; |
| tensor<fp16, [1, 77, 5120]> linear_124_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized, x = input_249_cast_fp16)[name = tensor<string, []>("linear_124_cast_fp16")]; |
| tensor<string, []> input_253_mode_0 = const()[name = tensor<string, []>("input_253_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_124_cast_fp16)[name = tensor<string, []>("input_253_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533703296))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540256960))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540257536)))]; |
| tensor<fp16, [1, 77, 1280]> linear_125_cast_fp16 = 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_253_cast_fp16)[name = tensor<string, []>("linear_125_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_125_cast_fp16)[name = tensor<string, []>("input_255_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_127_axes_0 = const()[name = tensor<string, []>("hidden_states_127_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540260160)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540262784)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_127_cast_fp16 = layer_norm(axes = hidden_states_127_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16, x = input_255_cast_fp16)[name = tensor<string, []>("hidden_states_127_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540265408))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541903872))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541904448)))]; |
| tensor<fp16, [1, 77, 1280]> linear_126_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_126_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541907072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543545536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543546112)))]; |
| tensor<fp16, [1, 77, 1280]> linear_127_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_127_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543548736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545187200))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545187776)))]; |
| tensor<fp16, [1, 77, 1280]> linear_128_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_128_cast_fp16")]; |
| tensor<int32, [4]> var_1310 = const()[name = tensor<string, []>("op_1310"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1311_cast_fp16 = reshape(shape = var_1310, x = linear_126_cast_fp16)[name = tensor<string, []>("op_1311_cast_fp16")]; |
| tensor<int32, [4]> var_1313 = const()[name = tensor<string, []>("op_1313"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1314_cast_fp16 = reshape(shape = var_1313, x = linear_127_cast_fp16)[name = tensor<string, []>("op_1314_cast_fp16")]; |
| tensor<int32, [4]> var_1316 = const()[name = tensor<string, []>("op_1316"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1317_cast_fp16 = reshape(shape = var_1316, x = linear_128_cast_fp16)[name = tensor<string, []>("op_1317_cast_fp16")]; |
| tensor<int32, [4]> value_states_87_perm_0 = const()[name = tensor<string, []>("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_21_cast_fp16 = mul(x = var_1311_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_21_cast_fp16")]; |
| tensor<bool, []> matmul_21_transpose_y_0 = const()[name = tensor<string, []>("matmul_21_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_21_transpose_x_0 = const()[name = tensor<string, []>("matmul_21_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_170_perm_0 = const()[name = tensor<string, []>("transpose_170_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_171_perm_0 = const()[name = tensor<string, []>("transpose_171_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_171 = transpose(perm = transpose_171_perm_0, x = var_1314_cast_fp16)[name = tensor<string, []>("transpose_234")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_170 = transpose(perm = transpose_170_perm_0, x = mul_21_cast_fp16)[name = tensor<string, []>("transpose_235")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_21_cast_fp16 = matmul(transpose_x = matmul_21_transpose_x_0, transpose_y = matmul_21_transpose_y_0, x = transpose_170, y = transpose_171)[name = tensor<string, []>("matmul_21_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_21_cast_fp16 = add(x = matmul_21_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_21_cast_fp16")]; |
| tensor<int32, []> softmax_21_axis_0 = const()[name = tensor<string, []>("softmax_21_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_21_cast_fp16 = softmax(axis = softmax_21_axis_0, x = add_21_cast_fp16)[name = tensor<string, []>("softmax_21_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_1317_cast_fp16)[name = tensor<string, []>("transpose_233")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_85_cast_fp16 = matmul(transpose_x = attn_output_85_transpose_x_0, transpose_y = attn_output_85_transpose_y_0, x = softmax_21_cast_fp16, y = value_states_87_cast_fp16)[name = tensor<string, []>("attn_output_85_cast_fp16")]; |
| tensor<int32, [4]> attn_output_87_perm_0 = const()[name = tensor<string, []>("attn_output_87_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1321 = const()[name = tensor<string, []>("op_1321"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_87_cast_fp16 = transpose(perm = attn_output_87_perm_0, x = attn_output_85_cast_fp16)[name = tensor<string, []>("transpose_232")]; |
| tensor<fp16, [1, 77, 1280]> input_257_cast_fp16 = reshape(shape = var_1321, x = attn_output_87_cast_fp16)[name = tensor<string, []>("input_257_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545190400))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546828864))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546829440)))]; |
| tensor<fp16, [1, 77, 1280]> linear_129_cast_fp16 = 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_257_cast_fp16)[name = tensor<string, []>("linear_129_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_259_cast_fp16 = add(x = input_255_cast_fp16, y = linear_129_cast_fp16)[name = tensor<string, []>("input_259_cast_fp16")]; |
| tensor<int32, [1]> input_261_axes_0 = const()[name = tensor<string, []>("input_261_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546832064)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546834688)))]; |
| tensor<fp16, [1, 77, 1280]> input_261_cast_fp16 = layer_norm(axes = input_261_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16, x = input_259_cast_fp16)[name = tensor<string, []>("input_261_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546837312))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553390976))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553391552)))]; |
| tensor<fp16, [1, 77, 5120]> linear_130_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized, x = input_261_cast_fp16)[name = tensor<string, []>("linear_130_cast_fp16")]; |
| tensor<string, []> input_265_mode_0 = const()[name = tensor<string, []>("input_265_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_265_cast_fp16 = gelu(mode = input_265_mode_0, x = linear_130_cast_fp16)[name = tensor<string, []>("input_265_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553401856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559955520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559956096)))]; |
| tensor<fp16, [1, 77, 1280]> linear_131_cast_fp16 = 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_265_cast_fp16)[name = tensor<string, []>("linear_131_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_267_cast_fp16 = add(x = input_259_cast_fp16, y = linear_131_cast_fp16)[name = tensor<string, []>("input_267_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_133_axes_0 = const()[name = tensor<string, []>("hidden_states_133_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559958720)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559961344)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_133_cast_fp16 = layer_norm(axes = hidden_states_133_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16, x = input_267_cast_fp16)[name = tensor<string, []>("hidden_states_133_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559963968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561602432))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561603008)))]; |
| tensor<fp16, [1, 77, 1280]> linear_132_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_132_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561605632))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563244096))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563244672)))]; |
| tensor<fp16, [1, 77, 1280]> linear_133_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_133_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563247296))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564885760))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564886336)))]; |
| tensor<fp16, [1, 77, 1280]> linear_134_cast_fp16 = 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_fp16)[name = tensor<string, []>("linear_134_cast_fp16")]; |
| tensor<int32, [4]> var_1365 = const()[name = tensor<string, []>("op_1365"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1366_cast_fp16 = reshape(shape = var_1365, x = linear_132_cast_fp16)[name = tensor<string, []>("op_1366_cast_fp16")]; |
| tensor<int32, [4]> var_1368 = const()[name = tensor<string, []>("op_1368"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1369_cast_fp16 = reshape(shape = var_1368, x = linear_133_cast_fp16)[name = tensor<string, []>("op_1369_cast_fp16")]; |
| tensor<int32, [4]> var_1371 = const()[name = tensor<string, []>("op_1371"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1372_cast_fp16 = reshape(shape = var_1371, x = linear_134_cast_fp16)[name = tensor<string, []>("op_1372_cast_fp16")]; |
| tensor<int32, [4]> value_states_91_perm_0 = const()[name = tensor<string, []>("value_states_91_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_22_cast_fp16 = mul(x = var_1366_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_22_cast_fp16")]; |
| tensor<bool, []> matmul_22_transpose_y_0 = const()[name = tensor<string, []>("matmul_22_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_22_transpose_x_0 = const()[name = tensor<string, []>("matmul_22_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_172_perm_0 = const()[name = tensor<string, []>("transpose_172_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_173_perm_0 = const()[name = tensor<string, []>("transpose_173_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_173 = transpose(perm = transpose_173_perm_0, x = var_1369_cast_fp16)[name = tensor<string, []>("transpose_230")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_172 = transpose(perm = transpose_172_perm_0, x = mul_22_cast_fp16)[name = tensor<string, []>("transpose_231")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_22_cast_fp16 = matmul(transpose_x = matmul_22_transpose_x_0, transpose_y = matmul_22_transpose_y_0, x = transpose_172, y = transpose_173)[name = tensor<string, []>("matmul_22_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_22_cast_fp16 = add(x = matmul_22_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_22_cast_fp16")]; |
| tensor<int32, []> softmax_22_axis_0 = const()[name = tensor<string, []>("softmax_22_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_22_cast_fp16 = softmax(axis = softmax_22_axis_0, x = add_22_cast_fp16)[name = tensor<string, []>("softmax_22_cast_fp16")]; |
| tensor<bool, []> attn_output_89_transpose_x_0 = const()[name = tensor<string, []>("attn_output_89_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_89_transpose_y_0 = const()[name = tensor<string, []>("attn_output_89_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_91_cast_fp16 = transpose(perm = value_states_91_perm_0, x = var_1372_cast_fp16)[name = tensor<string, []>("transpose_229")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_0, transpose_y = attn_output_89_transpose_y_0, x = softmax_22_cast_fp16, y = value_states_91_cast_fp16)[name = tensor<string, []>("attn_output_89_cast_fp16")]; |
| tensor<int32, [4]> attn_output_91_perm_0 = const()[name = tensor<string, []>("attn_output_91_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, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_91_cast_fp16 = transpose(perm = attn_output_91_perm_0, x = attn_output_89_cast_fp16)[name = tensor<string, []>("transpose_228")]; |
| tensor<fp16, [1, 77, 1280]> input_269_cast_fp16 = reshape(shape = var_1376, x = attn_output_91_cast_fp16)[name = tensor<string, []>("input_269_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564888960))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566527424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566528000)))]; |
| tensor<fp16, [1, 77, 1280]> linear_135_cast_fp16 = 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_269_cast_fp16)[name = tensor<string, []>("linear_135_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_271_cast_fp16 = add(x = input_267_cast_fp16, y = linear_135_cast_fp16)[name = tensor<string, []>("input_271_cast_fp16")]; |
| tensor<int32, [1]> input_273_axes_0 = const()[name = tensor<string, []>("input_273_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566530624)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566533248)))]; |
| tensor<fp16, [1, 77, 1280]> input_273_cast_fp16 = layer_norm(axes = input_273_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16, x = input_271_cast_fp16)[name = tensor<string, []>("input_273_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566535872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573089536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573090112)))]; |
| tensor<fp16, [1, 77, 5120]> linear_136_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = tensor<string, []>("linear_136_cast_fp16")]; |
| tensor<string, []> input_277_mode_0 = const()[name = tensor<string, []>("input_277_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_277_cast_fp16 = gelu(mode = input_277_mode_0, x = linear_136_cast_fp16)[name = tensor<string, []>("input_277_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573100416))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579654080))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579654656)))]; |
| tensor<fp16, [1, 77, 1280]> linear_137_cast_fp16 = 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_277_cast_fp16)[name = tensor<string, []>("linear_137_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_279_cast_fp16 = add(x = input_271_cast_fp16, y = linear_137_cast_fp16)[name = tensor<string, []>("input_279_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_139_axes_0 = const()[name = tensor<string, []>("hidden_states_139_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579657280)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579659904)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_139_cast_fp16 = layer_norm(axes = hidden_states_139_axes_0, beta = text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor<string, []>("hidden_states_139_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579662528))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581300992))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581301568)))]; |
| tensor<fp16, [1, 77, 1280]> linear_138_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_139_cast_fp16)[name = tensor<string, []>("linear_138_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581304192))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582942656))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582943232)))]; |
| tensor<fp16, [1, 77, 1280]> linear_139_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_139_cast_fp16)[name = tensor<string, []>("linear_139_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582945856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(584584320))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(584584896)))]; |
| tensor<fp16, [1, 77, 1280]> linear_140_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_139_cast_fp16)[name = tensor<string, []>("linear_140_cast_fp16")]; |
| tensor<int32, [4]> var_1420 = const()[name = tensor<string, []>("op_1420"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1421_cast_fp16 = reshape(shape = var_1420, x = linear_138_cast_fp16)[name = tensor<string, []>("op_1421_cast_fp16")]; |
| tensor<int32, [4]> var_1423 = const()[name = tensor<string, []>("op_1423"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1424_cast_fp16 = reshape(shape = var_1423, x = linear_139_cast_fp16)[name = tensor<string, []>("op_1424_cast_fp16")]; |
| tensor<int32, [4]> var_1426 = const()[name = tensor<string, []>("op_1426"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1427_cast_fp16 = reshape(shape = var_1426, x = linear_140_cast_fp16)[name = tensor<string, []>("op_1427_cast_fp16")]; |
| tensor<int32, [4]> value_states_95_perm_0 = const()[name = tensor<string, []>("value_states_95_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_23_cast_fp16 = mul(x = var_1421_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_23_cast_fp16")]; |
| tensor<bool, []> matmul_23_transpose_y_0 = const()[name = tensor<string, []>("matmul_23_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_23_transpose_x_0 = const()[name = tensor<string, []>("matmul_23_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_174_perm_0 = const()[name = tensor<string, []>("transpose_174_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_175_perm_0 = const()[name = tensor<string, []>("transpose_175_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_175 = transpose(perm = transpose_175_perm_0, x = var_1424_cast_fp16)[name = tensor<string, []>("transpose_226")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_174 = transpose(perm = transpose_174_perm_0, x = mul_23_cast_fp16)[name = tensor<string, []>("transpose_227")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_23_cast_fp16 = matmul(transpose_x = matmul_23_transpose_x_0, transpose_y = matmul_23_transpose_y_0, x = transpose_174, y = transpose_175)[name = tensor<string, []>("matmul_23_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_23_cast_fp16 = add(x = matmul_23_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_23_cast_fp16")]; |
| tensor<int32, []> softmax_23_axis_0 = const()[name = tensor<string, []>("softmax_23_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_23_cast_fp16 = softmax(axis = softmax_23_axis_0, x = add_23_cast_fp16)[name = tensor<string, []>("softmax_23_cast_fp16")]; |
| tensor<bool, []> attn_output_93_transpose_x_0 = const()[name = tensor<string, []>("attn_output_93_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_93_transpose_y_0 = const()[name = tensor<string, []>("attn_output_93_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_95_cast_fp16 = transpose(perm = value_states_95_perm_0, x = var_1427_cast_fp16)[name = tensor<string, []>("transpose_225")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_93_cast_fp16 = matmul(transpose_x = attn_output_93_transpose_x_0, transpose_y = attn_output_93_transpose_y_0, x = softmax_23_cast_fp16, y = value_states_95_cast_fp16)[name = tensor<string, []>("attn_output_93_cast_fp16")]; |
| 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_1431 = const()[name = tensor<string, []>("op_1431"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_95_cast_fp16 = transpose(perm = attn_output_95_perm_0, x = attn_output_93_cast_fp16)[name = tensor<string, []>("transpose_224")]; |
| tensor<fp16, [1, 77, 1280]> input_281_cast_fp16 = reshape(shape = var_1431, x = attn_output_95_cast_fp16)[name = tensor<string, []>("input_281_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(584587520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586225984))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586226560)))]; |
| tensor<fp16, [1, 77, 1280]> linear_141_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16_palettized, x = input_281_cast_fp16)[name = tensor<string, []>("linear_141_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_283_cast_fp16 = add(x = input_279_cast_fp16, y = linear_141_cast_fp16)[name = tensor<string, []>("input_283_cast_fp16")]; |
| tensor<int32, [1]> input_285_axes_0 = const()[name = tensor<string, []>("input_285_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586229184)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586231808)))]; |
| tensor<fp16, [1, 77, 1280]> input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16, x = input_283_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586234432))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592788096))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592788672)))]; |
| tensor<fp16, [1, 77, 5120]> linear_142_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor<string, []>("linear_142_cast_fp16")]; |
| tensor<string, []> input_289_mode_0 = const()[name = tensor<string, []>("input_289_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_142_cast_fp16)[name = tensor<string, []>("input_289_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592798976))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599352640))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599353216)))]; |
| tensor<fp16, [1, 77, 1280]> linear_143_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16_palettized, x = input_289_cast_fp16)[name = tensor<string, []>("linear_143_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_291_cast_fp16 = add(x = input_283_cast_fp16, y = linear_143_cast_fp16)[name = tensor<string, []>("input_291_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_145_axes_0 = const()[name = tensor<string, []>("hidden_states_145_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599355840)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599358464)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_145_cast_fp16 = layer_norm(axes = hidden_states_145_axes_0, beta = text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16, x = input_291_cast_fp16)[name = tensor<string, []>("hidden_states_145_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599361088))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600999552))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(601000128)))]; |
| tensor<fp16, [1, 77, 1280]> linear_144_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = tensor<string, []>("linear_144_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(601002752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602641216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602641792)))]; |
| tensor<fp16, [1, 77, 1280]> linear_145_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = tensor<string, []>("linear_145_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602644416))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604282880))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604283456)))]; |
| tensor<fp16, [1, 77, 1280]> linear_146_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = tensor<string, []>("linear_146_cast_fp16")]; |
| tensor<int32, [4]> var_1475 = const()[name = tensor<string, []>("op_1475"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1476_cast_fp16 = reshape(shape = var_1475, x = linear_144_cast_fp16)[name = tensor<string, []>("op_1476_cast_fp16")]; |
| tensor<int32, [4]> var_1478 = const()[name = tensor<string, []>("op_1478"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1479_cast_fp16 = reshape(shape = var_1478, x = linear_145_cast_fp16)[name = tensor<string, []>("op_1479_cast_fp16")]; |
| tensor<int32, [4]> var_1481 = const()[name = tensor<string, []>("op_1481"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1482_cast_fp16 = reshape(shape = var_1481, x = linear_146_cast_fp16)[name = tensor<string, []>("op_1482_cast_fp16")]; |
| tensor<int32, [4]> value_states_99_perm_0 = const()[name = tensor<string, []>("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_24_cast_fp16 = mul(x = var_1476_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_24_cast_fp16")]; |
| tensor<bool, []> matmul_24_transpose_y_0 = const()[name = tensor<string, []>("matmul_24_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_24_transpose_x_0 = const()[name = tensor<string, []>("matmul_24_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_176_perm_0 = const()[name = tensor<string, []>("transpose_176_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_177_perm_0 = const()[name = tensor<string, []>("transpose_177_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_177 = transpose(perm = transpose_177_perm_0, x = var_1479_cast_fp16)[name = tensor<string, []>("transpose_222")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_176 = transpose(perm = transpose_176_perm_0, x = mul_24_cast_fp16)[name = tensor<string, []>("transpose_223")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_24_cast_fp16 = matmul(transpose_x = matmul_24_transpose_x_0, transpose_y = matmul_24_transpose_y_0, x = transpose_176, y = transpose_177)[name = tensor<string, []>("matmul_24_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_24_cast_fp16 = add(x = matmul_24_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_24_cast_fp16")]; |
| tensor<int32, []> softmax_24_axis_0 = const()[name = tensor<string, []>("softmax_24_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_24_cast_fp16 = softmax(axis = softmax_24_axis_0, x = add_24_cast_fp16)[name = tensor<string, []>("softmax_24_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_1482_cast_fp16)[name = tensor<string, []>("transpose_221")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_0, transpose_y = attn_output_97_transpose_y_0, x = softmax_24_cast_fp16, y = value_states_99_cast_fp16)[name = tensor<string, []>("attn_output_97_cast_fp16")]; |
| tensor<int32, [4]> attn_output_99_perm_0 = const()[name = tensor<string, []>("attn_output_99_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1486 = const()[name = tensor<string, []>("op_1486"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_99_cast_fp16 = transpose(perm = attn_output_99_perm_0, x = attn_output_97_cast_fp16)[name = tensor<string, []>("transpose_220")]; |
| tensor<fp16, [1, 77, 1280]> input_293_cast_fp16 = reshape(shape = var_1486, x = attn_output_99_cast_fp16)[name = tensor<string, []>("input_293_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604286080))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605924544))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605925120)))]; |
| tensor<fp16, [1, 77, 1280]> linear_147_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16_palettized, x = input_293_cast_fp16)[name = tensor<string, []>("linear_147_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_295_cast_fp16 = add(x = input_291_cast_fp16, y = linear_147_cast_fp16)[name = tensor<string, []>("input_295_cast_fp16")]; |
| tensor<int32, [1]> input_297_axes_0 = const()[name = tensor<string, []>("input_297_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605927744)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605930368)))]; |
| tensor<fp16, [1, 77, 1280]> input_297_cast_fp16 = layer_norm(axes = input_297_axes_0, beta = text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16, x = input_295_cast_fp16)[name = tensor<string, []>("input_297_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605932992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612486656))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612487232)))]; |
| tensor<fp16, [1, 77, 5120]> linear_148_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16_palettized, x = input_297_cast_fp16)[name = tensor<string, []>("linear_148_cast_fp16")]; |
| tensor<string, []> input_301_mode_0 = const()[name = tensor<string, []>("input_301_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = linear_148_cast_fp16)[name = tensor<string, []>("input_301_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612497536))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619051200))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619051776)))]; |
| tensor<fp16, [1, 77, 1280]> linear_149_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor<string, []>("linear_149_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_303_cast_fp16 = add(x = input_295_cast_fp16, y = linear_149_cast_fp16)[name = tensor<string, []>("input_303_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_151_axes_0 = const()[name = tensor<string, []>("hidden_states_151_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619054400)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619057024)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_151_cast_fp16 = layer_norm(axes = hidden_states_151_axes_0, beta = text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16, x = input_303_cast_fp16)[name = tensor<string, []>("hidden_states_151_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619059648))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620698112))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620698688)))]; |
| tensor<fp16, [1, 77, 1280]> linear_150_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_151_cast_fp16)[name = tensor<string, []>("linear_150_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620701312))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622339776))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622340352)))]; |
| tensor<fp16, [1, 77, 1280]> linear_151_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_151_cast_fp16)[name = tensor<string, []>("linear_151_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622342976))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(623981440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(623982016)))]; |
| tensor<fp16, [1, 77, 1280]> linear_152_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_151_cast_fp16)[name = tensor<string, []>("linear_152_cast_fp16")]; |
| tensor<int32, [4]> var_1530 = const()[name = tensor<string, []>("op_1530"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1531_cast_fp16 = reshape(shape = var_1530, x = linear_150_cast_fp16)[name = tensor<string, []>("op_1531_cast_fp16")]; |
| tensor<int32, [4]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1534_cast_fp16 = reshape(shape = var_1533, x = linear_151_cast_fp16)[name = tensor<string, []>("op_1534_cast_fp16")]; |
| tensor<int32, [4]> var_1536 = const()[name = tensor<string, []>("op_1536"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1537_cast_fp16 = reshape(shape = var_1536, x = linear_152_cast_fp16)[name = tensor<string, []>("op_1537_cast_fp16")]; |
| tensor<int32, [4]> value_states_103_perm_0 = const()[name = tensor<string, []>("value_states_103_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_25_cast_fp16 = mul(x = var_1531_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_25_cast_fp16")]; |
| tensor<bool, []> matmul_25_transpose_y_0 = const()[name = tensor<string, []>("matmul_25_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_25_transpose_x_0 = const()[name = tensor<string, []>("matmul_25_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_178_perm_0 = const()[name = tensor<string, []>("transpose_178_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_179_perm_0 = const()[name = tensor<string, []>("transpose_179_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_179 = transpose(perm = transpose_179_perm_0, x = var_1534_cast_fp16)[name = tensor<string, []>("transpose_218")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_178 = transpose(perm = transpose_178_perm_0, x = mul_25_cast_fp16)[name = tensor<string, []>("transpose_219")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_25_cast_fp16 = matmul(transpose_x = matmul_25_transpose_x_0, transpose_y = matmul_25_transpose_y_0, x = transpose_178, y = transpose_179)[name = tensor<string, []>("matmul_25_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_25_cast_fp16 = add(x = matmul_25_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_25_cast_fp16")]; |
| tensor<int32, []> softmax_25_axis_0 = const()[name = tensor<string, []>("softmax_25_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_25_cast_fp16 = softmax(axis = softmax_25_axis_0, x = add_25_cast_fp16)[name = tensor<string, []>("softmax_25_cast_fp16")]; |
| tensor<bool, []> attn_output_101_transpose_x_0 = const()[name = tensor<string, []>("attn_output_101_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_101_transpose_y_0 = const()[name = tensor<string, []>("attn_output_101_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_103_cast_fp16 = transpose(perm = value_states_103_perm_0, x = var_1537_cast_fp16)[name = tensor<string, []>("transpose_217")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_101_cast_fp16 = matmul(transpose_x = attn_output_101_transpose_x_0, transpose_y = attn_output_101_transpose_y_0, x = softmax_25_cast_fp16, y = value_states_103_cast_fp16)[name = tensor<string, []>("attn_output_101_cast_fp16")]; |
| tensor<int32, [4]> attn_output_103_perm_0 = const()[name = tensor<string, []>("attn_output_103_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1541 = const()[name = tensor<string, []>("op_1541"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_103_cast_fp16 = transpose(perm = attn_output_103_perm_0, x = attn_output_101_cast_fp16)[name = tensor<string, []>("transpose_216")]; |
| tensor<fp16, [1, 77, 1280]> input_305_cast_fp16 = reshape(shape = var_1541, x = attn_output_103_cast_fp16)[name = tensor<string, []>("input_305_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(623984640))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625623104))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625623680)))]; |
| tensor<fp16, [1, 77, 1280]> linear_153_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16_palettized, x = input_305_cast_fp16)[name = tensor<string, []>("linear_153_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_307_cast_fp16 = add(x = input_303_cast_fp16, y = linear_153_cast_fp16)[name = tensor<string, []>("input_307_cast_fp16")]; |
| tensor<int32, [1]> input_309_axes_0 = const()[name = tensor<string, []>("input_309_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625626304)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625628928)))]; |
| tensor<fp16, [1, 77, 1280]> input_309_cast_fp16 = layer_norm(axes = input_309_axes_0, beta = text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16, x = input_307_cast_fp16)[name = tensor<string, []>("input_309_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625631552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632185216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632185792)))]; |
| tensor<fp16, [1, 77, 5120]> linear_154_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16_palettized, x = input_309_cast_fp16)[name = tensor<string, []>("linear_154_cast_fp16")]; |
| tensor<string, []> input_313_mode_0 = const()[name = tensor<string, []>("input_313_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = linear_154_cast_fp16)[name = tensor<string, []>("input_313_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632196096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638749760))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638750336)))]; |
| tensor<fp16, [1, 77, 1280]> linear_155_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16_palettized, x = input_313_cast_fp16)[name = tensor<string, []>("linear_155_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_315_cast_fp16 = add(x = input_307_cast_fp16, y = linear_155_cast_fp16)[name = tensor<string, []>("input_315_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_157_axes_0 = const()[name = tensor<string, []>("hidden_states_157_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638752960)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638755584)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_157_cast_fp16 = layer_norm(axes = hidden_states_157_axes_0, beta = text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16, x = input_315_cast_fp16)[name = tensor<string, []>("hidden_states_157_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638758208))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640396672))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640397248)))]; |
| tensor<fp16, [1, 77, 1280]> linear_156_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_157_cast_fp16)[name = tensor<string, []>("linear_156_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640399872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642038336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642038912)))]; |
| tensor<fp16, [1, 77, 1280]> linear_157_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_157_cast_fp16)[name = tensor<string, []>("linear_157_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642041536))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643680000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643680576)))]; |
| tensor<fp16, [1, 77, 1280]> linear_158_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_157_cast_fp16)[name = tensor<string, []>("linear_158_cast_fp16")]; |
| tensor<int32, [4]> var_1585 = const()[name = tensor<string, []>("op_1585"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1586_cast_fp16 = reshape(shape = var_1585, x = linear_156_cast_fp16)[name = tensor<string, []>("op_1586_cast_fp16")]; |
| tensor<int32, [4]> var_1588 = const()[name = tensor<string, []>("op_1588"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1589_cast_fp16 = reshape(shape = var_1588, x = linear_157_cast_fp16)[name = tensor<string, []>("op_1589_cast_fp16")]; |
| tensor<int32, [4]> var_1591 = const()[name = tensor<string, []>("op_1591"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1592_cast_fp16 = reshape(shape = var_1591, x = linear_158_cast_fp16)[name = tensor<string, []>("op_1592_cast_fp16")]; |
| tensor<int32, [4]> value_states_107_perm_0 = const()[name = tensor<string, []>("value_states_107_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_26_cast_fp16 = mul(x = var_1586_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_26_cast_fp16")]; |
| tensor<bool, []> matmul_26_transpose_y_0 = const()[name = tensor<string, []>("matmul_26_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_26_transpose_x_0 = const()[name = tensor<string, []>("matmul_26_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_180_perm_0 = const()[name = tensor<string, []>("transpose_180_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_181_perm_0 = const()[name = tensor<string, []>("transpose_181_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_181 = transpose(perm = transpose_181_perm_0, x = var_1589_cast_fp16)[name = tensor<string, []>("transpose_214")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_180 = transpose(perm = transpose_180_perm_0, x = mul_26_cast_fp16)[name = tensor<string, []>("transpose_215")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_26_cast_fp16 = matmul(transpose_x = matmul_26_transpose_x_0, transpose_y = matmul_26_transpose_y_0, x = transpose_180, y = transpose_181)[name = tensor<string, []>("matmul_26_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_26_cast_fp16 = add(x = matmul_26_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_26_cast_fp16")]; |
| tensor<int32, []> softmax_26_axis_0 = const()[name = tensor<string, []>("softmax_26_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_26_cast_fp16 = softmax(axis = softmax_26_axis_0, x = add_26_cast_fp16)[name = tensor<string, []>("softmax_26_cast_fp16")]; |
| tensor<bool, []> attn_output_105_transpose_x_0 = const()[name = tensor<string, []>("attn_output_105_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_105_transpose_y_0 = const()[name = tensor<string, []>("attn_output_105_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_107_cast_fp16 = transpose(perm = value_states_107_perm_0, x = var_1592_cast_fp16)[name = tensor<string, []>("transpose_213")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_0, transpose_y = attn_output_105_transpose_y_0, x = softmax_26_cast_fp16, y = value_states_107_cast_fp16)[name = tensor<string, []>("attn_output_105_cast_fp16")]; |
| 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_1596 = const()[name = tensor<string, []>("op_1596"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_107_cast_fp16 = transpose(perm = attn_output_107_perm_0, x = attn_output_105_cast_fp16)[name = tensor<string, []>("transpose_212")]; |
| tensor<fp16, [1, 77, 1280]> input_317_cast_fp16 = reshape(shape = var_1596, x = attn_output_107_cast_fp16)[name = tensor<string, []>("input_317_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643683200))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645321664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645322240)))]; |
| tensor<fp16, [1, 77, 1280]> linear_159_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16_palettized, x = input_317_cast_fp16)[name = tensor<string, []>("linear_159_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_319_cast_fp16 = add(x = input_315_cast_fp16, y = linear_159_cast_fp16)[name = tensor<string, []>("input_319_cast_fp16")]; |
| tensor<int32, [1]> input_321_axes_0 = const()[name = tensor<string, []>("input_321_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645324864)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645327488)))]; |
| tensor<fp16, [1, 77, 1280]> input_321_cast_fp16 = layer_norm(axes = input_321_axes_0, beta = text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16, x = input_319_cast_fp16)[name = tensor<string, []>("input_321_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645330112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651883776))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651884352)))]; |
| tensor<fp16, [1, 77, 5120]> linear_160_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16_palettized, x = input_321_cast_fp16)[name = tensor<string, []>("linear_160_cast_fp16")]; |
| tensor<string, []> input_325_mode_0 = const()[name = tensor<string, []>("input_325_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = linear_160_cast_fp16)[name = tensor<string, []>("input_325_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651894656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658448320))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658448896)))]; |
| tensor<fp16, [1, 77, 1280]> linear_161_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16_palettized, x = input_325_cast_fp16)[name = tensor<string, []>("linear_161_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_327_cast_fp16 = add(x = input_319_cast_fp16, y = linear_161_cast_fp16)[name = tensor<string, []>("input_327_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_163_axes_0 = const()[name = tensor<string, []>("hidden_states_163_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658451520)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658454144)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_163_cast_fp16 = layer_norm(axes = hidden_states_163_axes_0, beta = text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16, x = input_327_cast_fp16)[name = tensor<string, []>("hidden_states_163_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658456768))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660095232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660095808)))]; |
| tensor<fp16, [1, 77, 1280]> linear_162_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_163_cast_fp16)[name = tensor<string, []>("linear_162_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660098432))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661736896))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661737472)))]; |
| tensor<fp16, [1, 77, 1280]> linear_163_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_163_cast_fp16)[name = tensor<string, []>("linear_163_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661740096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(663378560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(663379136)))]; |
| tensor<fp16, [1, 77, 1280]> linear_164_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_163_cast_fp16)[name = tensor<string, []>("linear_164_cast_fp16")]; |
| tensor<int32, [4]> var_1640 = const()[name = tensor<string, []>("op_1640"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1641_cast_fp16 = reshape(shape = var_1640, x = linear_162_cast_fp16)[name = tensor<string, []>("op_1641_cast_fp16")]; |
| tensor<int32, [4]> var_1643 = const()[name = tensor<string, []>("op_1643"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1644_cast_fp16 = reshape(shape = var_1643, x = linear_163_cast_fp16)[name = tensor<string, []>("op_1644_cast_fp16")]; |
| tensor<int32, [4]> var_1646 = const()[name = tensor<string, []>("op_1646"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1647_cast_fp16 = reshape(shape = var_1646, x = linear_164_cast_fp16)[name = tensor<string, []>("op_1647_cast_fp16")]; |
| tensor<int32, [4]> value_states_111_perm_0 = const()[name = tensor<string, []>("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_27_cast_fp16 = mul(x = var_1641_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_27_cast_fp16")]; |
| tensor<bool, []> matmul_27_transpose_y_0 = const()[name = tensor<string, []>("matmul_27_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_27_transpose_x_0 = const()[name = tensor<string, []>("matmul_27_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_182_perm_0 = const()[name = tensor<string, []>("transpose_182_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_183_perm_0 = const()[name = tensor<string, []>("transpose_183_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_183 = transpose(perm = transpose_183_perm_0, x = var_1644_cast_fp16)[name = tensor<string, []>("transpose_210")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_182 = transpose(perm = transpose_182_perm_0, x = mul_27_cast_fp16)[name = tensor<string, []>("transpose_211")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_27_cast_fp16 = matmul(transpose_x = matmul_27_transpose_x_0, transpose_y = matmul_27_transpose_y_0, x = transpose_182, y = transpose_183)[name = tensor<string, []>("matmul_27_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_27_cast_fp16 = add(x = matmul_27_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_27_cast_fp16")]; |
| tensor<int32, []> softmax_27_axis_0 = const()[name = tensor<string, []>("softmax_27_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_27_cast_fp16 = softmax(axis = softmax_27_axis_0, x = add_27_cast_fp16)[name = tensor<string, []>("softmax_27_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_1647_cast_fp16)[name = tensor<string, []>("transpose_209")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_109_cast_fp16 = matmul(transpose_x = attn_output_109_transpose_x_0, transpose_y = attn_output_109_transpose_y_0, x = softmax_27_cast_fp16, y = value_states_111_cast_fp16)[name = tensor<string, []>("attn_output_109_cast_fp16")]; |
| tensor<int32, [4]> attn_output_111_perm_0 = const()[name = tensor<string, []>("attn_output_111_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1651 = const()[name = tensor<string, []>("op_1651"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_111_cast_fp16 = transpose(perm = attn_output_111_perm_0, x = attn_output_109_cast_fp16)[name = tensor<string, []>("transpose_208")]; |
| tensor<fp16, [1, 77, 1280]> input_329_cast_fp16 = reshape(shape = var_1651, x = attn_output_111_cast_fp16)[name = tensor<string, []>("input_329_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(663381760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(665020224))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(665020800)))]; |
| tensor<fp16, [1, 77, 1280]> linear_165_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = tensor<string, []>("linear_165_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_331_cast_fp16 = add(x = input_327_cast_fp16, y = linear_165_cast_fp16)[name = tensor<string, []>("input_331_cast_fp16")]; |
| tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(665023424)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(665026048)))]; |
| tensor<fp16, [1, 77, 1280]> input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16, x = input_331_cast_fp16)[name = tensor<string, []>("input_333_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(665028672))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671582336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671582912)))]; |
| tensor<fp16, [1, 77, 5120]> linear_166_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor<string, []>("linear_166_cast_fp16")]; |
| tensor<string, []> input_337_mode_0 = const()[name = tensor<string, []>("input_337_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = linear_166_cast_fp16)[name = tensor<string, []>("input_337_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671593216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678146880))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678147456)))]; |
| tensor<fp16, [1, 77, 1280]> linear_167_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor<string, []>("linear_167_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_339_cast_fp16 = add(x = input_331_cast_fp16, y = linear_167_cast_fp16)[name = tensor<string, []>("input_339_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_169_axes_0 = const()[name = tensor<string, []>("hidden_states_169_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678150080)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678152704)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_169_cast_fp16 = layer_norm(axes = hidden_states_169_axes_0, beta = text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16, x = input_339_cast_fp16)[name = tensor<string, []>("hidden_states_169_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678155328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(679793792))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(679794368)))]; |
| tensor<fp16, [1, 77, 1280]> linear_168_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_169_cast_fp16)[name = tensor<string, []>("linear_168_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(679796992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681435456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681436032)))]; |
| tensor<fp16, [1, 77, 1280]> linear_169_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_169_cast_fp16)[name = tensor<string, []>("linear_169_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681438656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(683077120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(683077696)))]; |
| tensor<fp16, [1, 77, 1280]> linear_170_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_169_cast_fp16)[name = tensor<string, []>("linear_170_cast_fp16")]; |
| tensor<int32, [4]> var_1695 = const()[name = tensor<string, []>("op_1695"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1696_cast_fp16 = reshape(shape = var_1695, x = linear_168_cast_fp16)[name = tensor<string, []>("op_1696_cast_fp16")]; |
| tensor<int32, [4]> var_1698 = const()[name = tensor<string, []>("op_1698"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1699_cast_fp16 = reshape(shape = var_1698, x = linear_169_cast_fp16)[name = tensor<string, []>("op_1699_cast_fp16")]; |
| tensor<int32, [4]> var_1701 = const()[name = tensor<string, []>("op_1701"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1702_cast_fp16 = reshape(shape = var_1701, x = linear_170_cast_fp16)[name = tensor<string, []>("op_1702_cast_fp16")]; |
| tensor<int32, [4]> value_states_115_perm_0 = const()[name = tensor<string, []>("value_states_115_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_28_cast_fp16 = mul(x = var_1696_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_28_cast_fp16")]; |
| tensor<bool, []> matmul_28_transpose_y_0 = const()[name = tensor<string, []>("matmul_28_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_28_transpose_x_0 = const()[name = tensor<string, []>("matmul_28_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_184_perm_0 = const()[name = tensor<string, []>("transpose_184_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_185_perm_0 = const()[name = tensor<string, []>("transpose_185_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_185 = transpose(perm = transpose_185_perm_0, x = var_1699_cast_fp16)[name = tensor<string, []>("transpose_206")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_184 = transpose(perm = transpose_184_perm_0, x = mul_28_cast_fp16)[name = tensor<string, []>("transpose_207")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_28_cast_fp16 = matmul(transpose_x = matmul_28_transpose_x_0, transpose_y = matmul_28_transpose_y_0, x = transpose_184, y = transpose_185)[name = tensor<string, []>("matmul_28_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_28_cast_fp16 = add(x = matmul_28_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_28_cast_fp16")]; |
| tensor<int32, []> softmax_28_axis_0 = const()[name = tensor<string, []>("softmax_28_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_28_cast_fp16 = softmax(axis = softmax_28_axis_0, x = add_28_cast_fp16)[name = tensor<string, []>("softmax_28_cast_fp16")]; |
| tensor<bool, []> attn_output_113_transpose_x_0 = const()[name = tensor<string, []>("attn_output_113_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_113_transpose_y_0 = const()[name = tensor<string, []>("attn_output_113_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_115_cast_fp16 = transpose(perm = value_states_115_perm_0, x = var_1702_cast_fp16)[name = tensor<string, []>("transpose_205")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_0, transpose_y = attn_output_113_transpose_y_0, x = softmax_28_cast_fp16, y = value_states_115_cast_fp16)[name = tensor<string, []>("attn_output_113_cast_fp16")]; |
| tensor<int32, [4]> attn_output_115_perm_0 = const()[name = tensor<string, []>("attn_output_115_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1706 = const()[name = tensor<string, []>("op_1706"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_115_cast_fp16 = transpose(perm = attn_output_115_perm_0, x = attn_output_113_cast_fp16)[name = tensor<string, []>("transpose_204")]; |
| tensor<fp16, [1, 77, 1280]> input_341_cast_fp16 = reshape(shape = var_1706, x = attn_output_115_cast_fp16)[name = tensor<string, []>("input_341_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(683080320))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684718784))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684719360)))]; |
| tensor<fp16, [1, 77, 1280]> linear_171_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16_palettized, x = input_341_cast_fp16)[name = tensor<string, []>("linear_171_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_343_cast_fp16 = add(x = input_339_cast_fp16, y = linear_171_cast_fp16)[name = tensor<string, []>("input_343_cast_fp16")]; |
| tensor<int32, [1]> input_345_axes_0 = const()[name = tensor<string, []>("input_345_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684721984)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684724608)))]; |
| tensor<fp16, [1, 77, 1280]> input_345_cast_fp16 = layer_norm(axes = input_345_axes_0, beta = text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16, x = input_343_cast_fp16)[name = tensor<string, []>("input_345_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684727232))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691280896))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691281472)))]; |
| tensor<fp16, [1, 77, 5120]> linear_172_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = tensor<string, []>("linear_172_cast_fp16")]; |
| tensor<string, []> input_349_mode_0 = const()[name = tensor<string, []>("input_349_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_349_cast_fp16 = gelu(mode = input_349_mode_0, x = linear_172_cast_fp16)[name = tensor<string, []>("input_349_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691291776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697845440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697846016)))]; |
| tensor<fp16, [1, 77, 1280]> linear_173_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = tensor<string, []>("linear_173_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_173_cast_fp16)[name = tensor<string, []>("input_351_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_175_axes_0 = const()[name = tensor<string, []>("hidden_states_175_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697848640)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697851264)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_175_cast_fp16 = layer_norm(axes = hidden_states_175_axes_0, beta = text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16, x = input_351_cast_fp16)[name = tensor<string, []>("hidden_states_175_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697853888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(699492352))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(699492928)))]; |
| tensor<fp16, [1, 77, 1280]> linear_174_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_175_cast_fp16)[name = tensor<string, []>("linear_174_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(699495552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701134016))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701134592)))]; |
| tensor<fp16, [1, 77, 1280]> linear_175_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_175_cast_fp16)[name = tensor<string, []>("linear_175_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701137216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(702775680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(702776256)))]; |
| tensor<fp16, [1, 77, 1280]> linear_176_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_175_cast_fp16)[name = tensor<string, []>("linear_176_cast_fp16")]; |
| tensor<int32, [4]> var_1750 = const()[name = tensor<string, []>("op_1750"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1751_cast_fp16 = reshape(shape = var_1750, x = linear_174_cast_fp16)[name = tensor<string, []>("op_1751_cast_fp16")]; |
| tensor<int32, [4]> var_1753 = const()[name = tensor<string, []>("op_1753"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1754_cast_fp16 = reshape(shape = var_1753, x = linear_175_cast_fp16)[name = tensor<string, []>("op_1754_cast_fp16")]; |
| tensor<int32, [4]> var_1756 = const()[name = tensor<string, []>("op_1756"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1757_cast_fp16 = reshape(shape = var_1756, x = linear_176_cast_fp16)[name = tensor<string, []>("op_1757_cast_fp16")]; |
| tensor<int32, [4]> value_states_119_perm_0 = const()[name = tensor<string, []>("value_states_119_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_29_cast_fp16 = mul(x = var_1751_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_29_cast_fp16")]; |
| tensor<bool, []> matmul_29_transpose_y_0 = const()[name = tensor<string, []>("matmul_29_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_29_transpose_x_0 = const()[name = tensor<string, []>("matmul_29_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_186_perm_0 = const()[name = tensor<string, []>("transpose_186_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_187_perm_0 = const()[name = tensor<string, []>("transpose_187_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_187 = transpose(perm = transpose_187_perm_0, x = var_1754_cast_fp16)[name = tensor<string, []>("transpose_202")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_186 = transpose(perm = transpose_186_perm_0, x = mul_29_cast_fp16)[name = tensor<string, []>("transpose_203")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_29_cast_fp16 = matmul(transpose_x = matmul_29_transpose_x_0, transpose_y = matmul_29_transpose_y_0, x = transpose_186, y = transpose_187)[name = tensor<string, []>("matmul_29_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_29_cast_fp16 = add(x = matmul_29_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_29_cast_fp16")]; |
| tensor<int32, []> softmax_29_axis_0 = const()[name = tensor<string, []>("softmax_29_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_29_cast_fp16 = softmax(axis = softmax_29_axis_0, x = add_29_cast_fp16)[name = tensor<string, []>("softmax_29_cast_fp16")]; |
| tensor<bool, []> attn_output_117_transpose_x_0 = const()[name = tensor<string, []>("attn_output_117_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_117_transpose_y_0 = const()[name = tensor<string, []>("attn_output_117_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_119_cast_fp16 = transpose(perm = value_states_119_perm_0, x = var_1757_cast_fp16)[name = tensor<string, []>("transpose_201")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_117_cast_fp16 = matmul(transpose_x = attn_output_117_transpose_x_0, transpose_y = attn_output_117_transpose_y_0, x = softmax_29_cast_fp16, y = value_states_119_cast_fp16)[name = tensor<string, []>("attn_output_117_cast_fp16")]; |
| 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_1761 = const()[name = tensor<string, []>("op_1761"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_119_cast_fp16 = transpose(perm = attn_output_119_perm_0, x = attn_output_117_cast_fp16)[name = tensor<string, []>("transpose_200")]; |
| tensor<fp16, [1, 77, 1280]> input_353_cast_fp16 = reshape(shape = var_1761, x = attn_output_119_cast_fp16)[name = tensor<string, []>("input_353_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(702778880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704417344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704417920)))]; |
| tensor<fp16, [1, 77, 1280]> linear_177_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor<string, []>("linear_177_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_355_cast_fp16 = add(x = input_351_cast_fp16, y = linear_177_cast_fp16)[name = tensor<string, []>("input_355_cast_fp16")]; |
| tensor<int32, [1]> input_357_axes_0 = const()[name = tensor<string, []>("input_357_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704420544)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704423168)))]; |
| tensor<fp16, [1, 77, 1280]> input_357_cast_fp16 = layer_norm(axes = input_357_axes_0, beta = text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("input_357_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704425792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710979456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710980032)))]; |
| tensor<fp16, [1, 77, 5120]> linear_178_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16_palettized, x = input_357_cast_fp16)[name = tensor<string, []>("linear_178_cast_fp16")]; |
| tensor<string, []> input_361_mode_0 = const()[name = tensor<string, []>("input_361_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_361_cast_fp16 = gelu(mode = input_361_mode_0, x = linear_178_cast_fp16)[name = tensor<string, []>("input_361_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710990336))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717544000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717544576)))]; |
| tensor<fp16, [1, 77, 1280]> linear_179_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = tensor<string, []>("linear_179_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_363_cast_fp16 = add(x = input_355_cast_fp16, y = linear_179_cast_fp16)[name = tensor<string, []>("input_363_cast_fp16")]; |
| tensor<int32, [1]> hidden_states_181_axes_0 = const()[name = tensor<string, []>("hidden_states_181_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717547200)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717549824)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_181_cast_fp16 = layer_norm(axes = hidden_states_181_axes_0, beta = text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16, x = input_363_cast_fp16)[name = tensor<string, []>("hidden_states_181_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717552448))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719190912))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719191488)))]; |
| tensor<fp16, [1, 77, 1280]> linear_180_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = tensor<string, []>("linear_180_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719194112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720832576))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720833152)))]; |
| tensor<fp16, [1, 77, 1280]> linear_181_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = tensor<string, []>("linear_181_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720835776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722474240))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722474816)))]; |
| tensor<fp16, [1, 77, 1280]> linear_182_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = tensor<string, []>("linear_182_cast_fp16")]; |
| tensor<int32, [4]> var_1805 = const()[name = tensor<string, []>("op_1805"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1806_cast_fp16 = reshape(shape = var_1805, x = linear_180_cast_fp16)[name = tensor<string, []>("op_1806_cast_fp16")]; |
| tensor<int32, [4]> var_1808 = const()[name = tensor<string, []>("op_1808"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1809_cast_fp16 = reshape(shape = var_1808, x = linear_181_cast_fp16)[name = tensor<string, []>("op_1809_cast_fp16")]; |
| tensor<int32, [4]> var_1811 = const()[name = tensor<string, []>("op_1811"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1812_cast_fp16 = reshape(shape = var_1811, x = linear_182_cast_fp16)[name = tensor<string, []>("op_1812_cast_fp16")]; |
| tensor<int32, [4]> value_states_123_perm_0 = const()[name = tensor<string, []>("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_30_cast_fp16 = mul(x = var_1806_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_30_cast_fp16")]; |
| tensor<bool, []> matmul_30_transpose_y_0 = const()[name = tensor<string, []>("matmul_30_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_30_transpose_x_0 = const()[name = tensor<string, []>("matmul_30_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_188_perm_0 = const()[name = tensor<string, []>("transpose_188_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_189_perm_0 = const()[name = tensor<string, []>("transpose_189_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_189 = transpose(perm = transpose_189_perm_0, x = var_1809_cast_fp16)[name = tensor<string, []>("transpose_198")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_188 = transpose(perm = transpose_188_perm_0, x = mul_30_cast_fp16)[name = tensor<string, []>("transpose_199")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_30_cast_fp16 = matmul(transpose_x = matmul_30_transpose_x_0, transpose_y = matmul_30_transpose_y_0, x = transpose_188, y = transpose_189)[name = tensor<string, []>("matmul_30_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_30_cast_fp16 = add(x = matmul_30_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_30_cast_fp16")]; |
| tensor<int32, []> softmax_30_axis_0 = const()[name = tensor<string, []>("softmax_30_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_30_cast_fp16 = softmax(axis = softmax_30_axis_0, x = add_30_cast_fp16)[name = tensor<string, []>("softmax_30_cast_fp16")]; |
| 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, [1, 20, 77, 64]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_1812_cast_fp16)[name = tensor<string, []>("transpose_197")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_0, transpose_y = attn_output_121_transpose_y_0, x = softmax_30_cast_fp16, y = value_states_123_cast_fp16)[name = tensor<string, []>("attn_output_121_cast_fp16")]; |
| tensor<int32, [4]> attn_output_123_perm_0 = const()[name = tensor<string, []>("attn_output_123_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1816 = const()[name = tensor<string, []>("op_1816"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_123_cast_fp16 = transpose(perm = attn_output_123_perm_0, x = attn_output_121_cast_fp16)[name = tensor<string, []>("transpose_196")]; |
| tensor<fp16, [1, 77, 1280]> input_365_cast_fp16 = reshape(shape = var_1816, x = attn_output_123_cast_fp16)[name = tensor<string, []>("input_365_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722477440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724115904))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724116480)))]; |
| tensor<fp16, [1, 77, 1280]> linear_183_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16_palettized, x = input_365_cast_fp16)[name = tensor<string, []>("linear_183_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_367_cast_fp16 = add(x = input_363_cast_fp16, y = linear_183_cast_fp16)[name = tensor<string, []>("input_367_cast_fp16")]; |
| tensor<int32, [1]> input_369_axes_0 = const()[name = tensor<string, []>("input_369_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724119104)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724121728)))]; |
| tensor<fp16, [1, 77, 1280]> input_369_cast_fp16 = layer_norm(axes = input_369_axes_0, beta = text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16, x = input_367_cast_fp16)[name = tensor<string, []>("input_369_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724124352))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730678016))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730678592)))]; |
| tensor<fp16, [1, 77, 5120]> linear_184_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = tensor<string, []>("linear_184_cast_fp16")]; |
| tensor<string, []> input_373_mode_0 = const()[name = tensor<string, []>("input_373_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_373_cast_fp16 = gelu(mode = input_373_mode_0, x = linear_184_cast_fp16)[name = tensor<string, []>("input_373_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730688896))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737242560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737243136)))]; |
| tensor<fp16, [1, 77, 1280]> linear_185_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = tensor<string, []>("linear_185_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_375_cast_fp16 = add(x = input_367_cast_fp16, y = linear_185_cast_fp16)[name = tensor<string, []>("input_375_cast_fp16")]; |
| tensor<string, []> input_375_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("input_375_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")]; |
| tensor<int32, [1]> hidden_states_187_axes_0 = const()[name = tensor<string, []>("hidden_states_187_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737245760)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737248384)))]; |
| tensor<fp16, [1, 77, 1280]> hidden_states_187_cast_fp16 = layer_norm(axes = hidden_states_187_axes_0, beta = text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16, x = input_375_cast_fp16)[name = tensor<string, []>("hidden_states_187_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737251008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(738889472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(738890048)))]; |
| tensor<fp16, [1, 77, 1280]> linear_186_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor<string, []>("linear_186_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(738892672))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(740531136))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(740531712)))]; |
| tensor<fp16, [1, 77, 1280]> linear_187_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor<string, []>("linear_187_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(740534336))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742172800))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742173376)))]; |
| tensor<fp16, [1, 77, 1280]> linear_188_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor<string, []>("linear_188_cast_fp16")]; |
| tensor<int32, [4]> var_1860 = const()[name = tensor<string, []>("op_1860"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1861_cast_fp16 = reshape(shape = var_1860, x = linear_186_cast_fp16)[name = tensor<string, []>("op_1861_cast_fp16")]; |
| tensor<int32, [4]> var_1863 = const()[name = tensor<string, []>("op_1863"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1864_cast_fp16 = reshape(shape = var_1863, x = linear_187_cast_fp16)[name = tensor<string, []>("op_1864_cast_fp16")]; |
| tensor<int32, [4]> var_1866 = const()[name = tensor<string, []>("op_1866"), val = tensor<int32, [4]>([1, -1, 20, 64])]; |
| tensor<fp16, [1, 77, 20, 64]> var_1867_cast_fp16 = reshape(shape = var_1866, x = linear_188_cast_fp16)[name = tensor<string, []>("op_1867_cast_fp16")]; |
| tensor<int32, [4]> value_states_perm_0 = const()[name = tensor<string, []>("value_states_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 77, 20, 64]> mul_31_cast_fp16 = mul(x = var_1861_cast_fp16, y = var_17_to_fp16)[name = tensor<string, []>("mul_31_cast_fp16")]; |
| tensor<bool, []> matmul_31_transpose_y_0 = const()[name = tensor<string, []>("matmul_31_transpose_y_0"), val = tensor<bool, []>(true)]; |
| tensor<bool, []> matmul_31_transpose_x_0 = const()[name = tensor<string, []>("matmul_31_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_190_perm_0 = const()[name = tensor<string, []>("transpose_190_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_191_perm_0 = const()[name = tensor<string, []>("transpose_191_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_191 = transpose(perm = transpose_191_perm_0, x = var_1864_cast_fp16)[name = tensor<string, []>("transpose_194")]; |
| tensor<fp16, [1, 20, 77, 64]> transpose_190 = transpose(perm = transpose_190_perm_0, x = mul_31_cast_fp16)[name = tensor<string, []>("transpose_195")]; |
| tensor<fp16, [1, 20, 77, 77]> matmul_31_cast_fp16 = matmul(transpose_x = matmul_31_transpose_x_0, transpose_y = matmul_31_transpose_y_0, x = transpose_190, y = transpose_191)[name = tensor<string, []>("matmul_31_cast_fp16")]; |
| tensor<fp16, [1, 20, 77, 77]> add_31_cast_fp16 = add(x = matmul_31_cast_fp16, y = op_59_to_fp16_palettized)[name = tensor<string, []>("add_31_cast_fp16")]; |
| tensor<int32, []> softmax_31_axis_0 = const()[name = tensor<string, []>("softmax_31_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp16, [1, 20, 77, 77]> softmax_31_cast_fp16 = softmax(axis = softmax_31_axis_0, x = add_31_cast_fp16)[name = tensor<string, []>("softmax_31_cast_fp16")]; |
| tensor<bool, []> attn_output_125_transpose_x_0 = const()[name = tensor<string, []>("attn_output_125_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> attn_output_125_transpose_y_0 = const()[name = tensor<string, []>("attn_output_125_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, 20, 77, 64]> value_states_cast_fp16 = transpose(perm = value_states_perm_0, x = var_1867_cast_fp16)[name = tensor<string, []>("transpose_193")]; |
| tensor<fp16, [1, 20, 77, 64]> attn_output_125_cast_fp16 = matmul(transpose_x = attn_output_125_transpose_x_0, transpose_y = attn_output_125_transpose_y_0, x = softmax_31_cast_fp16, y = value_states_cast_fp16)[name = tensor<string, []>("attn_output_125_cast_fp16")]; |
| 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_1871 = const()[name = tensor<string, []>("op_1871"), val = tensor<int32, [3]>([1, 77, 1280])]; |
| tensor<fp16, [1, 77, 20, 64]> attn_output_cast_fp16 = transpose(perm = attn_output_perm_0, x = attn_output_125_cast_fp16)[name = tensor<string, []>("transpose_192")]; |
| tensor<fp16, [1, 77, 1280]> input_377_cast_fp16 = reshape(shape = var_1871, x = attn_output_cast_fp16)[name = tensor<string, []>("input_377_cast_fp16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742176000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743814464))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743815040)))]; |
| tensor<fp16, [1, 77, 1280]> linear_189_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = tensor<string, []>("linear_189_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_379_cast_fp16 = add(x = input_375_cast_fp16, y = linear_189_cast_fp16)[name = tensor<string, []>("input_379_cast_fp16")]; |
| tensor<int32, [1]> input_381_axes_0 = const()[name = tensor<string, []>("input_381_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743817664)))]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743820288)))]; |
| tensor<fp16, [1, 77, 1280]> input_381_cast_fp16 = layer_norm(axes = input_381_axes_0, beta = text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16, x = input_379_cast_fp16)[name = tensor<string, []>("input_381_cast_fp16")]; |
| tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743822912))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750376576))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])]; |
| tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750377152)))]; |
| tensor<fp16, [1, 77, 5120]> linear_190_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = tensor<string, []>("linear_190_cast_fp16")]; |
| tensor<string, []> input_385_mode_0 = const()[name = tensor<string, []>("input_385_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 77, 5120]> input_385_cast_fp16 = gelu(mode = input_385_mode_0, x = linear_190_cast_fp16)[name = tensor<string, []>("input_385_cast_fp16")]; |
| tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750387456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756941120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])]; |
| tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756941696)))]; |
| tensor<fp16, [1, 77, 1280]> linear_191_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor<string, []>("linear_191_cast_fp16")]; |
| tensor<fp16, [1, 77, 1280]> input_387_cast_fp16 = add(x = input_379_cast_fp16, y = linear_191_cast_fp16)[name = tensor<string, []>("input_387_cast_fp16")]; |
| tensor<int32, [1]> last_hidden_state_axes_0 = const()[name = tensor<string, []>("last_hidden_state_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756944320)))]; |
| tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756946944)))]; |
| tensor<fp16, [1, 77, 1280]> last_hidden_state_cast_fp16 = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_387_cast_fp16)[name = tensor<string, []>("last_hidden_state_cast_fp16")]; |
| tensor<int32, [1]> var_1899 = const()[name = tensor<string, []>("op_1899"), val = tensor<int32, [1]>([0])]; |
| tensor<string, []> var_1901_output_dtype_0 = const()[name = tensor<string, []>("op_1901_output_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, [1]> var_1901 = reduce_argmax(axis = var_5, keep_dims = var_6, output_dtype = var_1901_output_dtype_0, x = cast_1)[name = tensor<string, []>("op_1901")]; |
| 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_1899, var_1901))[name = tensor<string, []>("stack_0")]; |
| tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, [1, 2]> greater_equal_0 = greater_equal(x = stack_0, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")]; |
| tensor<int32, [2]> slice_by_size_0 = const()[name = tensor<string, []>("slice_by_size_0"), val = tensor<int32, [2]>([1, 77])]; |
| tensor<int32, [1, 2]> add_32 = add(x = stack_0, y = slice_by_size_0)[name = tensor<string, []>("add_32")]; |
| tensor<int32, [1, 2]> select_0 = select(a = stack_0, b = add_32, cond = greater_equal_0)[name = tensor<string, []>("select_0")]; |
| tensor<int32, []> input_transpose_batch_dims_0 = const()[name = tensor<string, []>("input_transpose_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> input_transpose_validate_indices_0 = const()[name = tensor<string, []>("input_transpose_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<string, []> select_0_to_uint16_dtype_0 = const()[name = tensor<string, []>("select_0_to_uint16_dtype_0"), val = tensor<string, []>("uint16")]; |
| tensor<uint16, [1, 2]> select_0_to_uint16 = cast(dtype = select_0_to_uint16_dtype_0, x = select_0)[name = tensor<string, []>("cast_2")]; |
| tensor<fp16, [1, 1280]> input_transpose_cast_fp16_cast_uint16 = gather_nd(batch_dims = input_transpose_batch_dims_0, indices = select_0_to_uint16, validate_indices = input_transpose_validate_indices_0, x = last_hidden_state_cast_fp16)[name = tensor<string, []>("input_transpose_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1280, 1280]> text_encoder_text_projection_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756949568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758588032))), name = tensor<string, []>("text_encoder_text_projection_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])]; |
| tensor<fp16, [1280]> linear_192_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_192_bias_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758588608)))]; |
| tensor<fp16, [1, 1280]> linear_192_cast_fp16 = linear(bias = linear_192_bias_0_to_fp16, weight = text_encoder_text_projection_weight_to_fp16_palettized, x = input_transpose_cast_fp16_cast_uint16)[name = tensor<string, []>("linear_192_cast_fp16")]; |
| tensor<string, []> linear_192_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_192_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")]; |
| tensor<fp32, [1, 77, 1280]> hidden_embeds = cast(dtype = input_375_cast_fp16_to_fp32_dtype_0, x = input_375_cast_fp16)[name = tensor<string, []>("cast_0")]; |
| tensor<fp32, [1, 1280]> pooled_outputs = cast(dtype = linear_192_cast_fp16_to_fp32_dtype_0, x = linear_192_cast_fp16)[name = tensor<string, []>("cast_1")]; |
| } -> (hidden_embeds, pooled_outputs); |
| } |
