[New Sample] Add Some zero-shot-object-detection Model Computational Graph

samples/transformers-auto-model/IDEA-Research_grounding-dino-base/graph_hash.txt

@@ -0,0 +1 @@
+affc8c7cd95d8acc9ee5582af79eef03a74c19d6a08a54cd80ec10a6086cdba1

samples/transformers-auto-model/IDEA-Research_grounding-dino-base/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": false
+}

samples/transformers-auto-model/IDEA-Research_grounding-dino-base/weight_meta.py

@@ -0,0 +1,114 @@
+class Program_weight_tensor_meta_L_stack0_encoder_last_hidden_state_text:
+    name = "L_stack0_encoder_last_hidden_state_text"
+    shape = [1, 7, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 1.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_stack0_intermediate_hidden_states:
+    name = "L_stack0_intermediate_hidden_states"
+    shape = [1, 6, 900, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 1.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_stack0_init_reference_points:
+    name = "L_stack0_init_reference_points"
+    shape = [1, 900, 4]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.400
+    std = 0.296
+    data = None
+
+
+class Program_weight_tensor_meta_L_stack0_intermediate_reference_points:
+    name = "L_stack0_intermediate_reference_points"
+    shape = [1, 6, 900, 4]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.400
+    std = 0.296
+    data = None
+
+
+class Program_weight_tensor_meta_L_attention_mask_:
+    name = "L_attention_mask_"
+    shape = [1, 7]
+    dtype = "torch.int64"
+    device = "cuda:0"
+    mean = None
+    std = None
+    data = [1, 1, 1, 1, 1, 1, 1]
+
+
+class Program_weight_tensor_meta_L_self_modules_bbox_embed_modules_0_modules_layers_modules_0_parameters_weight_:
+    name = "L_self_modules_bbox_embed_modules_0_modules_layers_modules_0_parameters_weight_"
+    shape = [256, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = -0.000
+    std = 0.020
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_bbox_embed_modules_0_modules_layers_modules_0_parameters_bias_:
+    name = (
+        "L_self_modules_bbox_embed_modules_0_modules_layers_modules_0_parameters_bias_"
+    )
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_bbox_embed_modules_0_modules_layers_modules_1_parameters_weight_:
+    name = "L_self_modules_bbox_embed_modules_0_modules_layers_modules_1_parameters_weight_"
+    shape = [256, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.020
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_bbox_embed_modules_0_modules_layers_modules_1_parameters_bias_:
+    name = (
+        "L_self_modules_bbox_embed_modules_0_modules_layers_modules_1_parameters_bias_"
+    )
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_bbox_embed_modules_0_modules_layers_modules_2_parameters_weight_:
+    name = "L_self_modules_bbox_embed_modules_0_modules_layers_modules_2_parameters_weight_"
+    shape = [4, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_bbox_embed_modules_0_modules_layers_modules_2_parameters_bias_:
+    name = (
+        "L_self_modules_bbox_embed_modules_0_modules_layers_modules_2_parameters_bias_"
+    )
+    shape = [4]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = [0.000000, 0.000000, 0.000000, 0.000000]

samples/transformers-auto-model/IDEA-Research_grounding-dino-tiny/graph_hash.txt

@@ -0,0 +1 @@
+3eb7ef4fc44c784bf246435c08ca58a7f6defb71f4707d5dfa27e4bf629fc09d

samples/transformers-auto-model/IDEA-Research_grounding-dino-tiny/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": true
+}

samples/transformers-auto-model/IDEA-Research_grounding-dino-tiny/model.py

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+import torch
+
+
+class GraphModule(torch.nn.Module):
+    def forward(
+        self,
+        s61: torch.SymInt,
+        L_stack0_0_: torch.Tensor,
+        L_self_dropout: torch.Tensor,
+        L_third_residual_: torch.Tensor,
+        L_self_modules_encoder_attn_layer_norm_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_encoder_attn_layer_norm_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_encoder_attn_layer_norm_eps: torch.Tensor,
+        L_self_modules_fc1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_fc1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_activation_dropout: torch.Tensor,
+        L_self_modules_fc2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_fc2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_final_layer_norm_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_final_layer_norm_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_final_layer_norm_eps: torch.Tensor,
+    ):
+        l_stack0_0_ = L_stack0_0_
+        l_self_dropout = L_self_dropout
+        l_third_residual_ = L_third_residual_
+        l_self_modules_encoder_attn_layer_norm_parameters_weight_ = (
+            L_self_modules_encoder_attn_layer_norm_parameters_weight_
+        )
+        l_self_modules_encoder_attn_layer_norm_parameters_bias_ = (
+            L_self_modules_encoder_attn_layer_norm_parameters_bias_
+        )
+        l_self_modules_encoder_attn_layer_norm_eps = (
+            L_self_modules_encoder_attn_layer_norm_eps
+        )
+        l_self_modules_fc1_parameters_weight_ = L_self_modules_fc1_parameters_weight_
+        l_self_modules_fc1_parameters_bias_ = L_self_modules_fc1_parameters_bias_
+        l_self_activation_dropout = L_self_activation_dropout
+        l_self_modules_fc2_parameters_weight_ = L_self_modules_fc2_parameters_weight_
+        l_self_modules_fc2_parameters_bias_ = L_self_modules_fc2_parameters_bias_
+        l_self_modules_final_layer_norm_parameters_weight_ = (
+            L_self_modules_final_layer_norm_parameters_weight_
+        )
+        l_self_modules_final_layer_norm_parameters_bias_ = (
+            L_self_modules_final_layer_norm_parameters_bias_
+        )
+        l_self_modules_final_layer_norm_eps = L_self_modules_final_layer_norm_eps
+        item = l_self_dropout.item()
+        l_self_dropout = None
+        hidden_states = torch.nn.functional.dropout(l_stack0_0_, p=item, training=False)
+        l_stack0_0_ = None
+        hidden_states_1 = l_third_residual_ + hidden_states
+        l_third_residual_ = hidden_states = None
+        item_1 = l_self_modules_encoder_attn_layer_norm_eps.item()
+        l_self_modules_encoder_attn_layer_norm_eps = None
+        hidden_states_2 = torch.nn.functional.layer_norm(
+            hidden_states_1,
+            (256,),
+            l_self_modules_encoder_attn_layer_norm_parameters_weight_,
+            l_self_modules_encoder_attn_layer_norm_parameters_bias_,
+            item_1,
+        )
+        hidden_states_1 = (
+            l_self_modules_encoder_attn_layer_norm_parameters_weight_
+        ) = l_self_modules_encoder_attn_layer_norm_parameters_bias_ = item_1 = None
+        linear = torch._C._nn.linear(
+            hidden_states_2,
+            l_self_modules_fc1_parameters_weight_,
+            l_self_modules_fc1_parameters_bias_,
+        )
+        l_self_modules_fc1_parameters_weight_ = (
+            l_self_modules_fc1_parameters_bias_
+        ) = None
+        hidden_states_3 = torch.nn.functional.relu(linear, inplace=False)
+        linear = None
+        item_2 = l_self_activation_dropout.item()
+        l_self_activation_dropout = None
+        hidden_states_4 = torch.nn.functional.dropout(
+            hidden_states_3, p=item_2, training=False
+        )
+        hidden_states_3 = item_2 = None
+        hidden_states_5 = torch._C._nn.linear(
+            hidden_states_4,
+            l_self_modules_fc2_parameters_weight_,
+            l_self_modules_fc2_parameters_bias_,
+        )
+        hidden_states_4 = (
+            l_self_modules_fc2_parameters_weight_
+        ) = l_self_modules_fc2_parameters_bias_ = None
+        hidden_states_6 = torch.nn.functional.dropout(
+            hidden_states_5, p=item, training=False
+        )
+        hidden_states_5 = item = None
+        hidden_states_7 = hidden_states_2 + hidden_states_6
+        hidden_states_2 = hidden_states_6 = None
+        item_3 = l_self_modules_final_layer_norm_eps.item()
+        l_self_modules_final_layer_norm_eps = None
+        hidden_states_8 = torch.nn.functional.layer_norm(
+            hidden_states_7,
+            (256,),
+            l_self_modules_final_layer_norm_parameters_weight_,
+            l_self_modules_final_layer_norm_parameters_bias_,
+            item_3,
+        )
+        hidden_states_7 = (
+            l_self_modules_final_layer_norm_parameters_weight_
+        ) = l_self_modules_final_layer_norm_parameters_bias_ = item_3 = None
+        return (hidden_states_8,)

samples/transformers-auto-model/IDEA-Research_grounding-dino-tiny/weight_meta.py

@@ -0,0 +1,148 @@
+class Program_weight_tensor_meta_s61:
+    name = "s61"
+    shape = []
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    data = [4]
+
+
+class Program_weight_tensor_meta_L_stack0_0_:
+    name = "L_stack0_0_"
+    shape = [1, 900, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = -0.063
+    std = 0.819
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_dropout:
+    name = "L_self_dropout"
+    shape = []
+    dtype = "torch.float64"
+    device = "cpu"
+    mean = 0.100
+    std = 0.000
+    data = [0.100000]
+
+
+class Program_weight_tensor_meta_L_third_residual_:
+    name = "L_third_residual_"
+    shape = [1, 900, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = -0.000
+    std = 1.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_encoder_attn_layer_norm_parameters_weight_:
+    name = "L_self_modules_encoder_attn_layer_norm_parameters_weight_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 1.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_encoder_attn_layer_norm_parameters_bias_:
+    name = "L_self_modules_encoder_attn_layer_norm_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_encoder_attn_layer_norm_eps:
+    name = "L_self_modules_encoder_attn_layer_norm_eps"
+    shape = []
+    dtype = "torch.float64"
+    device = "cpu"
+    mean = 0.000
+    std = 0.000
+    data = [0.000010]
+
+
+class Program_weight_tensor_meta_L_self_modules_fc1_parameters_weight_:
+    name = "L_self_modules_fc1_parameters_weight_"
+    shape = [2048, 256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.020
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc1_parameters_bias_:
+    name = "L_self_modules_fc1_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_activation_dropout:
+    name = "L_self_activation_dropout"
+    shape = []
+    dtype = "torch.float64"
+    device = "cpu"
+    mean = 0.000
+    std = 0.000
+    data = [0.000000]
+
+
+class Program_weight_tensor_meta_L_self_modules_fc2_parameters_weight_:
+    name = "L_self_modules_fc2_parameters_weight_"
+    shape = [256, 2048]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = -0.000
+    std = 0.020
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc2_parameters_bias_:
+    name = "L_self_modules_fc2_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_final_layer_norm_parameters_weight_:
+    name = "L_self_modules_final_layer_norm_parameters_weight_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 1.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_final_layer_norm_parameters_bias_:
+    name = "L_self_modules_final_layer_norm_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cuda:0"
+    mean = 0.000
+    std = 0.000
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_final_layer_norm_eps:
+    name = "L_self_modules_final_layer_norm_eps"
+    shape = []
+    dtype = "torch.float64"
+    device = "cpu"
+    mean = 0.000
+    std = 0.000
+    data = [0.000010]

samples/transformers-auto-model/Thomasboosinger_owlvit-base-patch32/graph_hash.txt

@@ -0,0 +1 @@
+4be59c33ee4c3e088318f5641f37caefb8c1b421152f8beb426300cedc793ddb