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import numpy as np
import torch
from transformers import PhiConfig
from transformers.models.phi.modeling_phi import PhiModel
from experimental_experiment.helpers import pretty_onnx
from experimental_experiment.torch_interpreter import to_onnx, ExportOptions
from onnx_diagnostic.torch_export_patches import torch_export_patches
def ids_tensor(shape, vocab_size):
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(np.random.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
config = PhiConfig(
hidden_size=32,
num_hidden_layers=2,
vocab_size=1024,
intermediate_size=16,
max_position_embeddings=512,
num_attention_heads=2,
num_key_value_heads=2,
)
config._attn_implementation = "eager"
with torch.no_grad(), torch_export_patches(patch_transformers=True) as modificator:
model = PhiModel(config)
batch, seq, vocab_size = 2, 1024, 1024
input_ids = ids_tensor([batch, seq], vocab_size)
input_mask = torch.tril(torch.ones(batch, seq, dtype=torch.float32))
model(input_ids, input_mask)
onx = to_onnx(
model,
modificator((input_ids, input_mask)),
export_options=ExportOptions(decomposition_table="default"),
)
print(pretty_onnx(onx))
>>>
opset: domain='' version=18
input: name='input_ids' type=dtype('int64') shape=[2, 1024]
input: name='attention_mask' type=dtype('float32') shape=[2, 1024]
init: name='b_rotary_emb_inv_freq' type=float32 shape=(4,) -- array([1. , 0.1 , 0.01 , 0.001], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s4_2_1_1024_1024' type=int64 shape=(4,) -- array([ 2, 1, 1024, 1024])-- GraphBuilder.make_shape_from_results.shape
init: name='init7_s1_1024' type=int64 shape=(1,) -- array([1024]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_3' type=int64 shape=(1,) -- array([3]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s1_' type=float32 shape=(1,) -- array([-3.403e+38], dtype=float32)-- Opset.make_node.1/Small
init: name='init7_s4_2_1024_-1_16' type=int64 shape=(4,) -- array([ 2, 1024, -1, 16])-- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s3_2_1024_-1' type=int64 shape=(3,) -- array([ 2, 1024, -1])-- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s1_4' type=float32 shape=(1,) -- array([3.], dtype=float32)-- Opset.make_node.1/Small##Opset.make_node.1/Small
init: name='mul' type=float32 shape=(1024, 1024) -- GraphBuilder.constant_folding.from/fold(_onx_mul_triu0)##_onx_mul_triu0/
init: name='_reshape_init1_s_0' type=float32 shape=(1,) -- array([0.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_to_copy' type=float32 shape=(1, 1, 1024) -- GraphBuilder.constant_folding.from/fold(unsqueeze_9)##unsqueeze_9/
init: name='_onx_transpose_p_layers_0_self_attn_q_proj_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_0_self_attn_q_proj_weight)##p_layers_0_self_attn_q_proj_weight/DynamoInterpret.placeholder.1/P(layers.0.self_attn.q_proj.weight)
init: name='_onx_transpose_p_layers_0_self_attn_k_proj_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_0_self_attn_k_proj_weight)##p_layers_0_self_attn_k_proj_weight/DynamoInterpret.placeholder.1/P(layers.0.self_attn.k_proj.weight)
init: name='_onx_transpose_p_layers_0_self_attn_v_proj_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_0_self_attn_v_proj_weight)##p_layers_0_self_attn_v_proj_weight/DynamoInterpret.placeholder.1/P(layers.0.self_attn.v_proj.weight)
init: name='_reshape_init1_s_30' type=float32 shape=(1,) -- array([0.25], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_3,init7_s1_1)##init1_s_3/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_onx_transpose_p_layers_0_self_attn_dense_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_0_self_attn_dense_weight)##p_layers_0_self_attn_dense_weight/DynamoInterpret.placeholder.1/P(layers.0.self_attn.dense.weight)
init: name='_onx_transpose_p_layers_0_mlp_fc1_weight0' type=float32 shape=(32, 16)-- GraphBuilder.constant_folding.from/fold(p_layers_0_mlp_fc1_weight)##p_layers_0_mlp_fc1_weight/DynamoInterpret.placeholder.1/P(layers.0.mlp.fc1.weight)
init: name='_reshape_init1_s_40' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_4,init7_s1_1)##init1_s_4/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_50' type=float32 shape=(1,) -- array([0.045], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_5,init7_s1_1)##init1_s_5/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_60' type=float32 shape=(1,) -- array([0.798], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_6,init7_s1_1)##init1_s_6/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_203' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_2,init7_s1_1)##init1_s_2/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.0##shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_onx_transpose_p_layers_0_mlp_fc2_weight0' type=float32 shape=(16, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_0_mlp_fc2_weight)##p_layers_0_mlp_fc2_weight/DynamoInterpret.placeholder.1/P(layers.0.mlp.fc2.weight)
init: name='_onx_transpose_p_layers_1_self_attn_q_proj_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_1_self_attn_q_proj_weight)##p_layers_1_self_attn_q_proj_weight/DynamoInterpret.placeholder.1/P(layers.1.self_attn.q_proj.weight)
init: name='_onx_transpose_p_layers_1_self_attn_k_proj_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_1_self_attn_k_proj_weight)##p_layers_1_self_attn_k_proj_weight/DynamoInterpret.placeholder.1/P(layers.1.self_attn.k_proj.weight)
init: name='_onx_transpose_p_layers_1_self_attn_v_proj_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_1_self_attn_v_proj_weight)##p_layers_1_self_attn_v_proj_weight/DynamoInterpret.placeholder.1/P(layers.1.self_attn.v_proj.weight)
init: name='_reshape_init1_s_302' type=float32 shape=(1,) -- array([0.25], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_3,init7_s1_1)##init1_s_3/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_onx_transpose_p_layers_1_self_attn_dense_weight0' type=float32 shape=(32, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_1_self_attn_dense_weight)##p_layers_1_self_attn_dense_weight/DynamoInterpret.placeholder.1/P(layers.1.self_attn.dense.weight)
init: name='_onx_transpose_p_layers_1_mlp_fc1_weight0' type=float32 shape=(32, 16)-- GraphBuilder.constant_folding.from/fold(p_layers_1_mlp_fc1_weight)##p_layers_1_mlp_fc1_weight/DynamoInterpret.placeholder.1/P(layers.1.mlp.fc1.weight)
init: name='_reshape_init1_s_402' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_4,init7_s1_1)##init1_s_4/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_502' type=float32 shape=(1,) -- array([0.045], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_5,init7_s1_1)##init1_s_5/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_602' type=float32 shape=(1,) -- array([0.798], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_6,init7_s1_1)##init1_s_6/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_204' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_2,init7_s1_1)##init1_s_2/shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.1(mul_Tensor)##shape_type_compute._cast_inputs.0##shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_onx_transpose_p_layers_1_mlp_fc2_weight0' type=float32 shape=(16, 32)-- GraphBuilder.constant_folding.from/fold(p_layers_1_mlp_fc2_weight)##p_layers_1_mlp_fc2_weight/DynamoInterpret.placeholder.1/P(layers.1.mlp.fc2.weight)
init: name='init7_s2_0_1' type=int64 shape=(2,) -- array([0, 1]) -- UnsqueezeUnsqueezePattern.apply.new_axis
init: name='init7_s2_1_2' type=int64 shape=(2,) -- array([1, 2]) -- UnsqueezeUnsqueezePattern.apply.new_axis
init: name='init7_s2_0_2' type=int64 shape=(2,) -- array([0, 2]) -- UnsqueezeUnsqueezePattern.apply.new_axis
init: name='init1_s32_' type=float32 shape=(32,) -- LayerNormalizationPattern.apply.scale##LayerNormalizationPattern.apply.scale##LayerNormalizationPattern.apply.scale
init: name='init1_s32_2' type=float32 shape=(32,) -- LayerNormalizationPattern.apply.bias##LayerNormalizationPattern.apply.bias##LayerNormalizationPattern.apply.bias
init: name='init7_s2_8_8' type=int64 shape=(2,) -- array([8, 8]) -- SlicesSplitPattern.apply.splits##SlicesSplitPattern.apply.splits##SlicesSplitPattern.apply.splits##SlicesSplitPattern.apply.splits
init: name='init7_s2_4_4' type=int64 shape=(2,) -- array([4, 4]) -- SlicesSplitPattern.apply.splits##SlicesSplitPattern.apply.splits##SlicesSplitPattern.apply.splits##SlicesSplitPattern.apply.splits
init: name='embed_tokens.weight' type=float32 shape=(1024, 32) -- DynamoInterpret.placeholder.1/P(embed_tokens.weight)
Gather(embed_tokens.weight, input_ids) -> embedding
LayerNormalization(embedding, init1_s32_, init1_s32_2, axis=-1, epsilon=0.00, stash_type=1) -> _onx_div_sub_clone_100
MatMul(_onx_div_sub_clone_100, _onx_transpose_p_layers_0_self_attn_q_proj_weight0) -> _onx_matmul_layer_norm0
Reshape(_onx_matmul_layer_norm0, init7_s4_2_1024_-1_16) -> view_1
Transpose(view_1, perm=[0,2,1,3]) -> transpose_1
Split(transpose_1, init7_s2_8_8, axis=3) -> slice_26, slice_27
Split(slice_26, init7_s2_4_4, axis=3) -> slice_30, slice_31
Neg(slice_31) -> neg
Concat(neg, slice_30, axis=-1) -> cat_1
Unsqueeze(mul, init7_s2_0_1) -> unsqueeze_4
Expand(unsqueeze_4, init7_s4_2_1_1024_1024) -> expand_1
Slice(expand_1, init7_s1_0, init7_s1_1024, init7_s1_3) -> slice_8
Unsqueeze(attention_mask, init7_s2_1_2) -> unsqueeze_6
Add(slice_8, unsqueeze_6) -> add
Equal(add, _reshape_init1_s_0) -> eq
Where(eq, init1_s1_, slice_8) -> masked_fill
Slice(masked_fill, init7_s1_0, init7_s1_1024, init7_s1_3) -> slice_41
Unsqueeze(b_rotary_emb_inv_freq, init7_s2_0_2) -> unsqueeze_8
MatMul(unsqueeze_8, _to_copy) -> matmul
Transpose(matmul, perm=[0,2,1]) -> transpose
Concat(transpose, transpose, axis=-1) -> cat
Cos(cat) -> cos
Unsqueeze(cos, init7_s1_1) -> unsqueeze_10
Mul(slice_26, unsqueeze_10) -> mul_3
Sin(cat) -> sin
Unsqueeze(sin, init7_s1_1) -> unsqueeze_11
Mul(cat_1, unsqueeze_11) -> mul_4
Add(mul_3, mul_4) -> add_1
Concat(add_1, slice_27, axis=-1) -> cat_3
MatMul(_onx_div_sub_clone_100, _onx_transpose_p_layers_0_self_attn_k_proj_weight0) -> _onx_matmul_layer_norm02
Reshape(_onx_matmul_layer_norm02, init7_s4_2_1024_-1_16) -> view_2
Transpose(view_2, perm=[0,2,1,3]) -> transpose_2
Split(transpose_2, init7_s2_8_8, axis=3) -> slice_28, slice_29
Split(slice_28, init7_s2_4_4, axis=3) -> slice_32, slice_33
Neg(slice_33) -> neg_1
Concat(neg_1, slice_32, axis=-1) -> cat_2
Mul(cat_2, unsqueeze_11) -> mul_6
MatMul(_onx_div_sub_clone_100, _onx_transpose_p_layers_0_self_attn_v_proj_weight0) -> _onx_matmul_layer_norm03
Reshape(_onx_matmul_layer_norm03, init7_s4_2_1024_-1_16) -> view_3
Transpose(view_3, perm=[0,2,1,3]) -> output_3
Mul(slice_28, unsqueeze_10) -> mul_5
Add(mul_5, mul_6) -> add_2
Concat(add_2, slice_29, axis=-1) -> output_1
Transpose(output_1, perm=[0,1,3,2]) -> transpose_4
MatMul(cat_3, transpose_4) -> matmul_1
Mul(matmul_1, _reshape_init1_s_30) -> _onx_mul_matmul_10
Add(_onx_mul_matmul_10, slice_41) -> add_3
Softmax(add_3, axis=-1) -> softmax
MatMul(softmax, output_3) -> matmul_2
Transpose(matmul_2, perm=[0,2,1,3]) -> transpose_5
Reshape(transpose_5, init7_s3_2_1024_-1) -> view_4
MatMul(view_4, _onx_transpose_p_layers_0_self_attn_dense_weight0) -> _onx_matmul_view_40
MatMul(_onx_div_sub_clone_100, _onx_transpose_p_layers_0_mlp_fc1_weight0) -> _onx_matmul_layer_norm04
Mul(_onx_matmul_layer_norm04, _reshape_init1_s_40) -> _onx_mul_linear_40
Pow(_onx_matmul_layer_norm04, init1_s1_4) -> pow_1
Mul(pow_1, _reshape_init1_s_50) -> _onx_mul_pow_10
Add(_onx_matmul_layer_norm04, _onx_mul_pow_10) -> add_4
Mul(add_4, _reshape_init1_s_60) -> _onx_mul_add_40
Tanh(_onx_mul_add_40) -> tanh
Add(tanh, _reshape_init1_s_203) -> add_5
Mul(_onx_mul_linear_40, add_5) -> mul_11
MatMul(mul_11, _onx_transpose_p_layers_0_mlp_fc2_weight0) -> _onx_matmul_mul_110
Add(_onx_matmul_view_40, _onx_matmul_mul_110) -> add_6
Add(add_6, embedding) -> add_7
LayerNormalization(add_7, init1_s32_, init1_s32_2, axis=-1, epsilon=0.00, stash_type=1) -> _onx_div_sub_add_700
MatMul(_onx_div_sub_add_700, _onx_transpose_p_layers_1_self_attn_q_proj_weight0) -> _onx_matmul_layer_norm_10
Reshape(_onx_matmul_layer_norm_10, init7_s4_2_1024_-1_16) -> view_5
Transpose(view_5, perm=[0,2,1,3]) -> transpose_6
Split(transpose_6, init7_s2_8_8, axis=3) -> slice_42, slice_43
Split(slice_42, init7_s2_4_4, axis=3) -> slice_46, slice_47
Neg(slice_47) -> neg_2
Concat(neg_2, slice_46, axis=-1) -> cat_5
Mul(cat_5, unsqueeze_11) -> mul_13
MatMul(_onx_div_sub_add_700, _onx_transpose_p_layers_1_self_attn_k_proj_weight0) -> _onx_matmul_layer_norm_102
Reshape(_onx_matmul_layer_norm_102, init7_s4_2_1024_-1_16) -> view_6
Transpose(view_6, perm=[0,2,1,3]) -> transpose_7
Split(transpose_7, init7_s2_8_8, axis=3) -> slice_44, slice_45
Split(slice_44, init7_s2_4_4, axis=3) -> slice_48, slice_49
Neg(slice_49) -> neg_3
Concat(neg_3, slice_48, axis=-1) -> cat_6
Mul(cat_6, unsqueeze_11) -> mul_15
MatMul(_onx_div_sub_add_700, _onx_transpose_p_layers_1_self_attn_v_proj_weight0) -> _onx_matmul_layer_norm_103
Reshape(_onx_matmul_layer_norm_103, init7_s4_2_1024_-1_16) -> view_7
Transpose(view_7, perm=[0,2,1,3]) -> output_4
Mul(slice_42, unsqueeze_10) -> mul_12
Add(mul_12, mul_13) -> add_8
Concat(add_8, slice_43, axis=-1) -> cat_7
Mul(slice_44, unsqueeze_10) -> mul_14
Add(mul_14, mul_15) -> add_9
Concat(add_9, slice_45, axis=-1) -> output_2
Transpose(output_2, perm=[0,1,3,2]) -> transpose_9
MatMul(cat_7, transpose_9) -> matmul_3
Mul(matmul_3, _reshape_init1_s_302) -> _onx_mul_matmul_30
Add(_onx_mul_matmul_30, slice_41) -> add_10
Softmax(add_10, axis=-1) -> softmax_1
MatMul(softmax_1, output_4) -> matmul_4
Transpose(matmul_4, perm=[0,2,1,3]) -> transpose_10
Reshape(transpose_10, init7_s3_2_1024_-1) -> view_8
MatMul(view_8, _onx_transpose_p_layers_1_self_attn_dense_weight0) -> _onx_matmul_view_80
MatMul(_onx_div_sub_add_700, _onx_transpose_p_layers_1_mlp_fc1_weight0) -> _onx_matmul_layer_norm_104
Mul(_onx_matmul_layer_norm_104, _reshape_init1_s_402) -> _onx_mul_linear_100
Pow(_onx_matmul_layer_norm_104, init1_s1_4) -> pow_2
Mul(pow_2, _reshape_init1_s_502) -> _onx_mul_pow_20
Add(_onx_matmul_layer_norm_104, _onx_mul_pow_20) -> add_11
Mul(add_11, _reshape_init1_s_602) -> _onx_mul_add_110
Tanh(_onx_mul_add_110) -> tanh_1
Add(tanh_1, _reshape_init1_s_204) -> add_12
Mul(_onx_mul_linear_100, add_12) -> mul_20
MatMul(mul_20, _onx_transpose_p_layers_1_mlp_fc2_weight0) -> _onx_matmul_mul_200
Add(_onx_matmul_view_80, _onx_matmul_mul_200) -> add_13
Add(add_13, add_7) -> add_14
LayerNormalization(add_14, init1_s32_, init1_s32_2, axis=-1, epsilon=0.00, stash_type=1) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 1024, 32]
output: name='output_1' type=dtype('float32') shape=[2, 2, 1024, 16]
output: name='output_2' type=dtype('float32') shape=[2, 2, 1024, 16]
output: name='output_3' type=dtype('float32') shape=[2, 2, 1024, 16]
output: name='output_4' type=dtype('float32') shape=[2, 2, 1024, 16]