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qnn-model

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  1. .gitattributes +1 -0
  2. llama-3.2-3b-quantized-onnxruntime-qnn/dequantizer.onnx +3 -0
  3. llama-3.2-3b-quantized-onnxruntime-qnn/gen_qnn_ctx_onnx_model.py +366 -0
  4. llama-3.2-3b-quantized-onnxruntime-qnn/genai_config.json +507 -0
  5. llama-3.2-3b-quantized-onnxruntime-qnn/llama_v3_2_3b_chat_quantized_part_1_of_3.bin +3 -0
  6. llama-3.2-3b-quantized-onnxruntime-qnn/llama_v3_2_3b_chat_quantized_part_1_of_3.json +169 -0
  7. llama-3.2-3b-quantized-onnxruntime-qnn/llama_v3_2_3b_chat_quantized_part_2_of_3.bin +3 -0
  8. llama-3.2-3b-quantized-onnxruntime-qnn/llama_v3_2_3b_chat_quantized_part_2_of_3.json +0 -0
  9. llama-3.2-3b-quantized-onnxruntime-qnn/llama_v3_2_3b_chat_quantized_part_3_of_3.bin +3 -0
  10. llama-3.2-3b-quantized-onnxruntime-qnn/llama_v3_2_3b_chat_quantized_part_3_of_3.json +0 -0
  11. llama-3.2-3b-quantized-onnxruntime-qnn/position-processor.onnx +3 -0
  12. llama-3.2-3b-quantized-onnxruntime-qnn/prompt_1_of_3_qnn_ctx.onnx +3 -0
  13. llama-3.2-3b-quantized-onnxruntime-qnn/prompt_2_of_3_qnn_ctx.onnx +3 -0
  14. llama-3.2-3b-quantized-onnxruntime-qnn/prompt_3_of_3_qnn_ctx.onnx +3 -0
  15. llama-3.2-3b-quantized-onnxruntime-qnn/quantizer.onnx +3 -0
  16. llama-3.2-3b-quantized-onnxruntime-qnn/special_tokens_map.json +16 -0
  17. llama-3.2-3b-quantized-onnxruntime-qnn/token_1_of_3_qnn_ctx.onnx +3 -0
  18. llama-3.2-3b-quantized-onnxruntime-qnn/token_2_of_3_qnn_ctx.onnx +3 -0
  19. llama-3.2-3b-quantized-onnxruntime-qnn/token_3_of_3_qnn_ctx.onnx +3 -0
  20. llama-3.2-3b-quantized-onnxruntime-qnn/tokenizer.json +3 -0
  21. llama-3.2-3b-quantized-onnxruntime-qnn/tokenizer_config.json +2062 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ llama-3.2-3b-quantized-onnxruntime-qnn/tokenizer.json filter=lfs diff=lfs merge=lfs -text
llama-3.2-3b-quantized-onnxruntime-qnn/dequantizer.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:2df5e2c14be055deb40eb909670e187ae35ed321750c212ec30628b9619894f8
3
+ size 181
llama-3.2-3b-quantized-onnxruntime-qnn/gen_qnn_ctx_onnx_model.py ADDED
@@ -0,0 +1,366 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -------------------------------------------------------------------------
2
+ # Copyright (c) Microsoft Corporation. All rights reserved.
3
+ # Licensed under the MIT License.
4
+ # --------------------------------------------------------------------------
5
+
6
+ import json
7
+ from argparse import ArgumentParser
8
+
9
+ import onnx
10
+ from onnx import TensorProto, helper
11
+
12
+
13
+ class QnnTensorStruct:
14
+ def __init__(self):
15
+ self.name = ""
16
+ self.onnx_data_type = TensorProto.FLOAT
17
+ self.is_quantized = False
18
+ self.scale = 0.0
19
+ self.offset = 0
20
+ self.dim = []
21
+
22
+
23
+ def is_quantized_data_type(qnn_data_type, is_converter_json):
24
+ if is_converter_json:
25
+ # QNN_DATATYPE_UFIXED_POINT_8 QNN_DATATYPE_UFIXED_POINT_16 QNN_DATATYPE_FIXED_POINT_8 QNN_DATATYPE_FIXED_POINT_16
26
+ return qnn_data_type == 0x0408 or qnn_data_type == 0x0416 or qnn_data_type == 0x0308 or qnn_data_type == 0x0316
27
+ else:
28
+ return (
29
+ qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_8"
30
+ or qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_16"
31
+ or qnn_data_type == "QNN_DATATYPE_FIXED_POINT_8"
32
+ or qnn_data_type == "QNN_DATATYPE_FIXED_POINT_16"
33
+ )
34
+
35
+
36
+ def qnn_data_type_to_onnx_data_type(qnn_data_type, is_converter_json):
37
+ if is_converter_json:
38
+ # QNN_DATATYPE_UFIXED_POINT_8 QNN_DATATYPE_UINT_8
39
+ if qnn_data_type == 0x0408 or qnn_data_type == 0x0108:
40
+ return TensorProto.UINT8
41
+ # QNN_DATATYPE_UFIXED_POINT_16 QNN_DATATYPE_UINT_16
42
+ elif qnn_data_type == 0x0416 or qnn_data_type == 0x0116:
43
+ return TensorProto.UINT16
44
+ # QNN_DATATYPE_UFIXED_POINT_32 QNN_DATATYPE_UINT_32
45
+ elif qnn_data_type == 0x0432 or qnn_data_type == 0x0132:
46
+ return TensorProto.UINT32
47
+ # QNN_DATATYPE_UINT_64
48
+ elif qnn_data_type == 0x0164:
49
+ return TensorProto.UINT64
50
+ # QNN_DATATYPE_FIXED_POINT_8 QNN_DATATYPE_INT_8
51
+ elif qnn_data_type == 0x0308 or qnn_data_type == 0x0008:
52
+ return TensorProto.INT8
53
+ # QNN_DATATYPE_FIXED_POINT_16 QNN_DATATYPE_INT_16
54
+ elif qnn_data_type == 0x0316 or qnn_data_type == 0x0016:
55
+ return TensorProto.INT16
56
+ # QNN_DATATYPE_FIXED_POINT_32 QNN_DATATYPE_INT_32
57
+ elif qnn_data_type == 0x0332 or qnn_data_type == 0x0032:
58
+ return TensorProto.INT32
59
+ # QNN_DATATYPE_INT_64
60
+ elif qnn_data_type == 0x0064:
61
+ return TensorProto.INT64
62
+ # QNN_DATATYPE_FLOAT_16
63
+ elif qnn_data_type == 0x0216:
64
+ return TensorProto.FLOAT16
65
+ # QNN_DATATYPE_FLOAT_32
66
+ elif qnn_data_type == 0x0232:
67
+ return TensorProto.FLOAT
68
+ # QNN_DATATYPE_BOOL_8
69
+ elif qnn_data_type == 0x0508:
70
+ return TensorProto.BOOL
71
+ else:
72
+ return TensorProto.UNDEFINED
73
+ else:
74
+ # QNN_DATATYPE_UFIXED_POINT_8 QNN_DATATYPE_UINT_8
75
+ if qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_8" or qnn_data_type == "QNN_DATATYPE_UINT_8":
76
+ return TensorProto.UINT8
77
+ # QNN_DATATYPE_UFIXED_POINT_16 QNN_DATATYPE_UINT_16
78
+ elif qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_16" or qnn_data_type == "QNN_DATATYPE_UINT_16":
79
+ return TensorProto.UINT16
80
+ # QNN_DATATYPE_UFIXED_POINT_32 QNN_DATATYPE_UINT_32
81
+ elif qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_32" or qnn_data_type == "QNN_DATATYPE_UINT_32":
82
+ return TensorProto.UINT32
83
+ # QNN_DATATYPE_UINT_64
84
+ elif qnn_data_type == "QNN_DATATYPE_UINT_64":
85
+ return TensorProto.UINT64
86
+ # QNN_DATATYPE_FIXED_POINT_8 QNN_DATATYPE_INT_8
87
+ elif qnn_data_type == "QNN_DATATYPE_FIXED_POINT_8" or qnn_data_type == "QNN_DATATYPE_INT_8":
88
+ return TensorProto.INT8
89
+ # QNN_DATATYPE_FIXED_POINT_16 QNN_DATATYPE_INT_16
90
+ elif qnn_data_type == "QNN_DATATYPE_FIXED_POINT_16" or qnn_data_type == "QNN_DATATYPE_INT_16":
91
+ return TensorProto.INT16
92
+ # QNN_DATATYPE_FIXED_POINT_32 QNN_DATATYPE_INT_32
93
+ elif qnn_data_type == "QNN_DATATYPE_FIXED_POINT_32" or qnn_data_type == "QNN_DATATYPE_INT_32":
94
+ return TensorProto.INT32
95
+ # QNN_DATATYPE_INT_64
96
+ elif qnn_data_type == "QNN_DATATYPE_INT_64":
97
+ return TensorProto.INT64
98
+ # QNN_DATATYPE_FLOAT_16
99
+ elif qnn_data_type == "QNN_DATATYPE_FLOAT_16":
100
+ return TensorProto.FLOAT16
101
+ # QNN_DATATYPE_FLOAT_32
102
+ elif qnn_data_type == "QNN_DATATYPE_FLOAT_32":
103
+ return TensorProto.FLOAT
104
+ # QNN_DATATYPE_BOOL_8
105
+ elif qnn_data_type == "QNN_DATATYPE_BOOL_8":
106
+ return TensorProto.BOOL
107
+ else:
108
+ return TensorProto.UNDEFINED
109
+
110
+
111
+ def parse_qnn_converter_json_file(qnn_convert_json, qnn_input_tensor_dic, qnn_output_tensor_dic):
112
+ is_qnn_converter_json = True
113
+ for qnn_tensor_name, qnn_tensor_attribute in qnn_convert_json["graph"]["tensors"].items():
114
+ # type:0 - QNN input tensor, type:1 - QNN output tensor
115
+ assert (
116
+ "type" in qnn_tensor_attribute and "data_type" in qnn_tensor_attribute and "dims" in qnn_tensor_attribute
117
+ ), "QNN converted json file not valid. Can't find some keys from tensors"
118
+
119
+ # Get all graph inputs
120
+ if qnn_tensor_attribute["type"] == 0:
121
+ qnn_tensor = QnnTensorStruct()
122
+ qnn_tensor.name = qnn_tensor_name
123
+ qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(
124
+ qnn_tensor_attribute["data_type"], is_qnn_converter_json
125
+ )
126
+ qnn_tensor.is_quantized = is_quantized_data_type(qnn_tensor_attribute["data_type"], is_qnn_converter_json)
127
+ qnn_tensor.dim = qnn_tensor_attribute["dims"]
128
+ if (
129
+ qnn_tensor_attribute["quant_params"]["definition"] == 1
130
+ and qnn_tensor_attribute["quant_params"]["encoding"] == 0
131
+ ):
132
+ qnn_tensor.scale = qnn_tensor_attribute["quant_params"]["scale_offset"]["scale"]
133
+ qnn_tensor.offset = 0 - qnn_tensor_attribute["quant_params"]["scale_offset"]["offset"]
134
+ qnn_input_tensor_dic[qnn_tensor_name] = qnn_tensor
135
+
136
+ # Get all graph outputs
137
+ if qnn_tensor_attribute["type"] == 1:
138
+ qnn_tensor = QnnTensorStruct()
139
+ qnn_tensor.name = qnn_tensor_name
140
+ qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(
141
+ qnn_tensor_attribute["data_type"], is_qnn_converter_json
142
+ )
143
+ qnn_tensor.is_quantized = is_quantized_data_type(qnn_tensor_attribute["data_type"], is_qnn_converter_json)
144
+ qnn_tensor.dim = qnn_tensor_attribute["dims"]
145
+ if (
146
+ qnn_tensor_attribute["quant_params"]["definition"] == 1
147
+ and qnn_tensor_attribute["quant_params"]["encoding"] == 0
148
+ ):
149
+ qnn_tensor.scale = qnn_tensor_attribute["quant_params"]["scale_offset"]["scale"]
150
+ qnn_tensor.offset = 0 - qnn_tensor_attribute["quant_params"]["scale_offset"]["offset"]
151
+ qnn_output_tensor_dic[qnn_tensor_name] = qnn_tensor
152
+
153
+ assert len(qnn_input_tensor_dic) >= 1 and len(qnn_output_tensor_dic) >= 1, (
154
+ "Converted QNN model not valid. It should have at least 1 input & 1 output."
155
+ )
156
+
157
+
158
+ def generate_wrapper_onnx_file(
159
+ grap_name,
160
+ model_file_name,
161
+ qnn_input_tensor_dic,
162
+ qnn_output_tensor_dic,
163
+ disable_embed_mode,
164
+ qnn_ctx_file,
165
+ quantized_IO,
166
+ qnn_sdk_version="unknown",
167
+ ):
168
+ graph_nodes = []
169
+ ini_list = []
170
+ value_infos = []
171
+
172
+ model_inputs = []
173
+ for qnn_input in qnn_input_tensor_dic.values():
174
+ if qnn_input.is_quantized and not quantized_IO:
175
+ q_scale_input_name = qnn_input.name + "_scale"
176
+ q_offset_input_name = qnn_input.name + "_zp"
177
+ q_scale = helper.make_tensor(q_scale_input_name, TensorProto.FLOAT, [], [qnn_input.scale])
178
+ ini_list.append(q_scale)
179
+ q_offset = helper.make_tensor(q_offset_input_name, qnn_input.onnx_data_type, [], [qnn_input.offset])
180
+ ini_list.append(q_offset)
181
+ input_name = qnn_input.name + "_dq"
182
+
183
+ q_node = helper.make_node(
184
+ "QuantizeLinear",
185
+ name=qnn_input.name,
186
+ inputs=[input_name, q_scale_input_name, q_offset_input_name],
187
+ outputs=[qnn_input.name],
188
+ )
189
+
190
+ graph_nodes.append(q_node)
191
+ model_inputs.append(helper.make_tensor_value_info(input_name, TensorProto.FLOAT, qnn_input.dim))
192
+ value_infos.append(helper.make_tensor_value_info(qnn_input.name, qnn_input.onnx_data_type, qnn_input.dim))
193
+ else:
194
+ model_inputs.append(helper.make_tensor_value_info(qnn_input.name, qnn_input.onnx_data_type, qnn_input.dim))
195
+
196
+ if disable_embed_mode:
197
+ ep_cache_context_content = qnn_ctx_file
198
+ ctx_embed_mode = 0
199
+ else:
200
+ with open(qnn_ctx_file, "rb") as file:
201
+ ep_cache_context_content = file.read()
202
+ ctx_embed_mode = 1
203
+
204
+ qnn_ep_context_node = helper.make_node(
205
+ "EPContext",
206
+ name=grap_name,
207
+ inputs=qnn_input_tensor_dic.keys(),
208
+ outputs=qnn_output_tensor_dic.keys(),
209
+ ep_cache_context=ep_cache_context_content,
210
+ embed_mode=ctx_embed_mode,
211
+ ep_sdk_version=qnn_sdk_version,
212
+ source="Qnn",
213
+ domain="com.microsoft",
214
+ )
215
+ graph_nodes.append(qnn_ep_context_node)
216
+
217
+ model_outputs = []
218
+ for qnn_output in qnn_output_tensor_dic.values():
219
+ if qnn_output.is_quantized and not quantized_IO:
220
+ dq_scale_input_name = qnn_output.name + "_scale"
221
+ dq_offset_input_name = qnn_output.name + "_zp"
222
+ dq_scale = helper.make_tensor(dq_scale_input_name, TensorProto.FLOAT, [], [qnn_output.scale])
223
+ ini_list.append(dq_scale)
224
+ dq_offset = helper.make_tensor(dq_offset_input_name, qnn_output.onnx_data_type, [], [qnn_output.offset])
225
+ ini_list.append(dq_offset)
226
+ output_name = qnn_output.name + "_dq"
227
+
228
+ dq_node = helper.make_node(
229
+ "DequantizeLinear",
230
+ name=output_name,
231
+ inputs=[qnn_output.name, dq_scale_input_name, dq_offset_input_name],
232
+ outputs=[output_name],
233
+ )
234
+
235
+ graph_nodes.append(dq_node)
236
+ model_outputs.append(helper.make_tensor_value_info(output_name, TensorProto.FLOAT, qnn_output.dim))
237
+ value_infos.append(
238
+ helper.make_tensor_value_info(qnn_output.name, qnn_output.onnx_data_type, qnn_output.dim)
239
+ )
240
+ else:
241
+ model_outputs.append(
242
+ helper.make_tensor_value_info(qnn_output.name, qnn_output.onnx_data_type, qnn_output.dim)
243
+ )
244
+
245
+ graph_def = helper.make_graph(graph_nodes, "qnn-onnx-model", model_inputs, model_outputs, ini_list, "", value_infos)
246
+
247
+ model_def = helper.make_model(graph_def, producer_name="MS")
248
+
249
+ onnx.save(model_def, model_file_name)
250
+
251
+
252
+ # parse Qnn graph from the json file that extracted from context binary file
253
+ def parse_qnn_graph(qnn_graph, qnn_input_tensor_dic, qnn_output_tensor_dic):
254
+ is_qnn_converter_json = False
255
+ graph_name = qnn_graph["info"]["graphName"]
256
+ raw_inputs = qnn_graph["info"]["graphInputs"]
257
+ raw_outputs = qnn_graph["info"]["graphOutputs"]
258
+
259
+ for raw_input in raw_inputs:
260
+ tensor_info = raw_input["info"]
261
+ qnn_tensor = QnnTensorStruct()
262
+ qnn_tensor.name = tensor_info["name"]
263
+ qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(tensor_info["dataType"], is_qnn_converter_json)
264
+ qnn_tensor.is_quantized = is_quantized_data_type(tensor_info["dataType"], is_qnn_converter_json)
265
+ qnn_tensor.dim = tensor_info["dimensions"]
266
+ if (
267
+ tensor_info["quantizeParams"]["definition"] == "QNN_DEFINITION_DEFINED"
268
+ and tensor_info["quantizeParams"]["quantizationEncoding"] == "QNN_QUANTIZATION_ENCODING_SCALE_OFFSET"
269
+ ):
270
+ qnn_tensor.scale = tensor_info["quantizeParams"]["scaleOffset"]["scale"]
271
+ qnn_tensor.offset = 0 - tensor_info["quantizeParams"]["scaleOffset"]["offset"]
272
+ qnn_input_tensor_dic[qnn_tensor.name] = qnn_tensor
273
+
274
+ for raw_output in raw_outputs:
275
+ tensor_info = raw_output["info"]
276
+ qnn_tensor = QnnTensorStruct()
277
+ qnn_tensor.name = tensor_info["name"]
278
+ qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(tensor_info["dataType"], is_qnn_converter_json)
279
+ qnn_tensor.is_quantized = is_quantized_data_type(tensor_info["dataType"], is_qnn_converter_json)
280
+ qnn_tensor.dim = tensor_info["dimensions"]
281
+ if (
282
+ tensor_info["quantizeParams"]["definition"] == "QNN_DEFINITION_DEFINED"
283
+ and tensor_info["quantizeParams"]["quantizationEncoding"] == "QNN_QUANTIZATION_ENCODING_SCALE_OFFSET"
284
+ ):
285
+ qnn_tensor.scale = tensor_info["quantizeParams"]["scaleOffset"]["scale"]
286
+ qnn_tensor.offset = 0 - tensor_info["quantizeParams"]["scaleOffset"]["offset"]
287
+ qnn_output_tensor_dic[qnn_tensor.name] = qnn_tensor
288
+
289
+ assert len(qnn_input_tensor_dic) >= 1 and len(qnn_output_tensor_dic) >= 1, (
290
+ "Converted QNN model not valid. It should have at least 1 input & 1 output."
291
+ )
292
+
293
+ return graph_name
294
+
295
+
296
+ # Onnxruntime QNN EP can support context binary file generated by QNN tool chain. However QNN generated context binary file
297
+ # uses channel last data layout and 8 bits or 16 bits for input and output.
298
+ # This script gets the QNN model input & output information from QNN converted model_net.json file, compare them with Onnx model
299
+ # and inserts Cast, Transpose nodes to Onnx model if required
300
+ def main():
301
+ parser = ArgumentParser("Generate Onnx model which includes the QNN context binary.")
302
+ parser.add_argument("-b", "--qnn_bin", help="Required. Path to Qnn context binary file.", required=True, type=str)
303
+ parser.add_argument(
304
+ "-q", "--qnn_json", help="Required. Path to Qnn converted model_net.json file.", required=True, type=str
305
+ )
306
+ parser.add_argument(
307
+ "--disable_embed_mode",
308
+ action="store_true",
309
+ default=False,
310
+ help="Set embed_mode=1 which mean embed Qnn context binary into the onnx model. Otherwise, set context binary file path in the onnx model",
311
+ )
312
+ parser.add_argument(
313
+ "--quantized_IO",
314
+ action="store_true",
315
+ default=False,
316
+ help="QNN converted context binary use quantized data as graph inputs and outputs. Will keep it if quantized_IO=True, otherwise, will insert Q and DQ nodes accordingly to make the graph inputs & outputs as float32 data type.",
317
+ )
318
+ args = parser.parse_args()
319
+
320
+ # Parse Qnn model_net.json file to get the graph input output information
321
+
322
+ with open(args.qnn_json) as qnn_json_file:
323
+ qnn_json_obj = json.load(qnn_json_file)
324
+ if "graph" in qnn_json_obj and "tensors" in qnn_json_obj["graph"]:
325
+ print("This json file is from Qnn converter")
326
+ qnn_input_tensor_dic = {}
327
+ qnn_output_tensor_dic = {}
328
+ parse_qnn_converter_json_file(qnn_json_obj, qnn_input_tensor_dic, qnn_output_tensor_dic)
329
+
330
+ generate_wrapper_onnx_file(
331
+ "QnnContext",
332
+ args.qnn_json.replace(".json", "_qnn_ctx.onnx"),
333
+ qnn_input_tensor_dic,
334
+ qnn_output_tensor_dic,
335
+ args.disable_embed_mode,
336
+ args.qnn_bin,
337
+ args.quantized_IO,
338
+ )
339
+ elif "info" in qnn_json_obj and "graphs" in qnn_json_obj["info"]:
340
+ print("This json file is extracted from QNN context binary file")
341
+ qnn_version = qnn_json_obj["info"]["buildId"]
342
+ for qnn_graph in qnn_json_obj["info"]["graphs"]:
343
+ qnn_input_tensor_dic = {}
344
+ qnn_output_tensor_dic = {}
345
+ graph_name = parse_qnn_graph(qnn_graph, qnn_input_tensor_dic, qnn_output_tensor_dic)
346
+
347
+ ctx_file_name = graph_name + "_qnn_ctx.onnx"
348
+ if not args.quantized_IO:
349
+ ctx_file_name = ctx_file_name.replace(".onnx", "_fp32_io.onnx")
350
+
351
+ generate_wrapper_onnx_file(
352
+ graph_name,
353
+ ctx_file_name,
354
+ qnn_input_tensor_dic,
355
+ qnn_output_tensor_dic,
356
+ args.disable_embed_mode,
357
+ args.qnn_bin,
358
+ args.quantized_IO,
359
+ qnn_version,
360
+ )
361
+ else:
362
+ print("json file unrecoginized.")
363
+
364
+
365
+ if __name__ == "__main__":
366
+ main()
llama-3.2-3b-quantized-onnxruntime-qnn/genai_config.json ADDED
@@ -0,0 +1,507 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "model": {
3
+ "bos_token_id": 128000,
4
+ "context_length": 4096,
5
+ "decoder": {
6
+ "session_options": {
7
+ "log_id": "onnxruntime-genai",
8
+ "provider_options": []
9
+ },
10
+ "head_size": 128,
11
+ "hidden_size": 3072,
12
+ "inputs": {
13
+ "input_ids": "input_ids",
14
+ "attention_mask": "attention_mask_before_processor",
15
+ "position_ids": "position_ids",
16
+ "past_key_names": "past_key_%d_in",
17
+ "past_value_names": "past_value_%d_in"
18
+ },
19
+ "outputs": {
20
+ "logits": "logits_dequantized",
21
+ "present_key_names": "past_key_%d_out",
22
+ "present_value_names": "past_value_%d_out"
23
+ },
24
+ "num_attention_heads": 24,
25
+ "num_hidden_layers": 28,
26
+ "num_key_value_heads": 8,
27
+ "sliding_window_key_value_cache": {
28
+ "window_size": 128,
29
+ "pad_value": 128
30
+ },
31
+ "pipeline": [
32
+ {
33
+ "position-processor": {
34
+ "filename": "position-processor.onnx",
35
+ "inputs": [
36
+ "attention_mask_before_processor",
37
+ "position_ids"
38
+ ],
39
+ "outputs": [
40
+ "attention_mask_before_quantizer",
41
+ "position_ids_cos_before_quantizer",
42
+ "position_ids_sin_before_quantizer"
43
+ ],
44
+ "session_options": {
45
+ "log_id": "onnxruntime-genai.position_processor",
46
+ "provider_options": [
47
+ {}
48
+ ]
49
+ },
50
+ "run_on_token_gen": false
51
+ },
52
+ "position-shifter": {
53
+ "filename": "position-shifter.onnx",
54
+ "inputs": [
55
+ "attention_mask_before_processor",
56
+ "position_ids"
57
+ ],
58
+ "outputs": [
59
+ "attention_mask_before_quantizer",
60
+ "position_ids_cos_before_quantizer",
61
+ "position_ids_sin_before_quantizer"
62
+ ],
63
+ "session_options": {
64
+ "log_id": "onnxruntime-genai.position_shifter",
65
+ "provider_options": [
66
+ {}
67
+ ]
68
+ },
69
+ "run_on_prompt": false
70
+ },
71
+ "quantizer": {
72
+ "filename": "quantizer.onnx",
73
+ "inputs": [
74
+ "attention_mask_before_quantizer",
75
+ "position_ids_cos_before_quantizer",
76
+ "position_ids_sin_before_quantizer"
77
+ ],
78
+ "outputs": [
79
+ "attention_mask",
80
+ "position_ids_cos",
81
+ "position_ids_sin"
82
+ ],
83
+ "session_options": {
84
+ "log_id": "onnxruntime-genai.quantizer",
85
+ "provider_options": [
86
+ {}
87
+ ]
88
+ }
89
+ },
90
+ "prompt-processor-1": {
91
+ "filename": "prompt_1_of_3_qnn_ctx.onnx",
92
+ "inputs": [
93
+ "input_ids"
94
+ ],
95
+ "outputs": [
96
+ "_model_model_embed_tokens_Gather_output_0"
97
+ ],
98
+ "session_options": {
99
+ "log_id": "onnxruntime-genai.pp1",
100
+ "provider_options": [
101
+ {
102
+ "qnn": {
103
+ "backend_path": "QnnHtp.dll",
104
+ "htp_performance_mode": "burst",
105
+ "enable_htp_shared_memory_allocator": "1",
106
+ "qnn_context_priority": "high"
107
+ }
108
+ }
109
+ ]
110
+ },
111
+ "run_on_token_gen": false
112
+ },
113
+ "prompt-processor-2": {
114
+ "filename": "prompt_2_of_3_qnn_ctx.onnx",
115
+ "inputs": [
116
+ "_model_model_embed_tokens_Gather_output_0",
117
+ "attention_mask",
118
+ "position_ids_cos",
119
+ "position_ids_sin",
120
+ "past_key_0_in",
121
+ "past_value_0_in",
122
+ "past_key_1_in",
123
+ "past_value_1_in",
124
+ "past_key_2_in",
125
+ "past_value_2_in",
126
+ "past_key_3_in",
127
+ "past_value_3_in",
128
+ "past_key_4_in",
129
+ "past_value_4_in",
130
+ "past_key_5_in",
131
+ "past_value_5_in",
132
+ "past_key_6_in",
133
+ "past_value_6_in",
134
+ "past_key_7_in",
135
+ "past_value_7_in",
136
+ "past_key_8_in",
137
+ "past_value_8_in",
138
+ "past_key_9_in",
139
+ "past_value_9_in",
140
+ "past_key_10_in",
141
+ "past_value_10_in",
142
+ "past_key_11_in",
143
+ "past_value_11_in",
144
+ "past_key_12_in",
145
+ "past_value_12_in",
146
+ "past_key_13_in",
147
+ "past_value_13_in"
148
+ ],
149
+ "outputs": [
150
+ "_model_model_layers_13_Add_1_output_0",
151
+ "past_key_0_out",
152
+ "past_value_0_out",
153
+ "past_key_1_out",
154
+ "past_value_1_out",
155
+ "past_key_2_out",
156
+ "past_value_2_out",
157
+ "past_key_3_out",
158
+ "past_value_3_out",
159
+ "past_key_4_out",
160
+ "past_value_4_out",
161
+ "past_key_5_out",
162
+ "past_value_5_out",
163
+ "past_key_6_out",
164
+ "past_value_6_out",
165
+ "past_key_7_out",
166
+ "past_value_7_out",
167
+ "past_key_8_out",
168
+ "past_value_8_out",
169
+ "past_key_9_out",
170
+ "past_value_9_out",
171
+ "past_key_10_out",
172
+ "past_value_10_out",
173
+ "past_key_11_out",
174
+ "past_value_11_out",
175
+ "past_key_12_out",
176
+ "past_value_12_out",
177
+ "past_key_13_out",
178
+ "past_value_13_out"
179
+ ],
180
+ "session_options": {
181
+ "log_id": "onnxruntime-genai.pp2",
182
+ "provider_options": [
183
+ {
184
+ "qnn": {
185
+ "backend_path": "QnnHtp.dll",
186
+ "htp_performance_mode": "burst",
187
+ "enable_htp_shared_memory_allocator": "1",
188
+ "qnn_context_priority": "high"
189
+ }
190
+ }
191
+ ]
192
+ },
193
+ "run_on_token_gen": false
194
+ },
195
+ "prompt-processor-3": {
196
+ "filename": "prompt_3_of_3_qnn_ctx.onnx",
197
+ "inputs": [
198
+ "_model_model_layers_13_Add_1_output_0",
199
+ "attention_mask",
200
+ "position_ids_cos",
201
+ "position_ids_sin",
202
+ "past_key_14_in",
203
+ "past_value_14_in",
204
+ "past_key_15_in",
205
+ "past_value_15_in",
206
+ "past_key_16_in",
207
+ "past_value_16_in",
208
+ "past_key_17_in",
209
+ "past_value_17_in",
210
+ "past_key_18_in",
211
+ "past_value_18_in",
212
+ "past_key_19_in",
213
+ "past_value_19_in",
214
+ "past_key_20_in",
215
+ "past_value_20_in",
216
+ "past_key_21_in",
217
+ "past_value_21_in",
218
+ "past_key_22_in",
219
+ "past_value_22_in",
220
+ "past_key_23_in",
221
+ "past_value_23_in",
222
+ "past_key_24_in",
223
+ "past_value_24_in",
224
+ "past_key_25_in",
225
+ "past_value_25_in",
226
+ "past_key_26_in",
227
+ "past_value_26_in",
228
+ "past_key_27_in",
229
+ "past_value_27_in"
230
+ ],
231
+ "outputs": [
232
+ "logits",
233
+ "past_key_14_out",
234
+ "past_value_14_out",
235
+ "past_key_15_out",
236
+ "past_value_15_out",
237
+ "past_key_16_out",
238
+ "past_value_16_out",
239
+ "past_key_17_out",
240
+ "past_value_17_out",
241
+ "past_key_18_out",
242
+ "past_value_18_out",
243
+ "past_key_19_out",
244
+ "past_value_19_out",
245
+ "past_key_20_out",
246
+ "past_value_20_out",
247
+ "past_key_21_out",
248
+ "past_value_21_out",
249
+ "past_key_22_out",
250
+ "past_value_22_out",
251
+ "past_key_23_out",
252
+ "past_value_23_out",
253
+ "past_key_24_out",
254
+ "past_value_24_out",
255
+ "past_key_25_out",
256
+ "past_value_25_out",
257
+ "past_key_26_out",
258
+ "past_value_26_out",
259
+ "past_key_27_out",
260
+ "past_value_27_out"
261
+ ],
262
+ "session_options": {
263
+ "log_id": "onnxruntime-genai.pp3",
264
+ "provider_options": [
265
+ {
266
+ "qnn": {
267
+ "backend_path": "QnnHtp.dll",
268
+ "htp_performance_mode": "burst",
269
+ "enable_htp_shared_memory_allocator": "1",
270
+ "qnn_context_priority": "high"
271
+ }
272
+ }
273
+ ]
274
+ },
275
+ "run_on_token_gen": false
276
+ },
277
+ "token-generator-1": {
278
+ "filename": "token_1_of_3_qnn_ctx.onnx",
279
+ "inputs": [
280
+ "input_ids"
281
+ ],
282
+ "outputs": [
283
+ "_model_model_embed_tokens_Gather_output_0"
284
+ ],
285
+ "session_options": {
286
+ "log_id": "onnxruntime-genai.tg1",
287
+ "provider_options": [
288
+ {
289
+ "qnn": {
290
+ "backend_path": "QnnHtp.dll",
291
+ "htp_performance_mode": "burst",
292
+ "enable_htp_shared_memory_allocator": "1",
293
+ "qnn_context_priority": "high"
294
+ }
295
+ }
296
+ ]
297
+ },
298
+ "run_on_prompt": false
299
+ },
300
+ "token-generator-2": {
301
+ "filename": "token_2_of_3_qnn_ctx.onnx",
302
+ "inputs": [
303
+ "_model_model_embed_tokens_Gather_output_0",
304
+ "attention_mask",
305
+ "position_ids_cos",
306
+ "position_ids_sin",
307
+ "past_key_0_in",
308
+ "past_value_0_in",
309
+ "past_key_1_in",
310
+ "past_value_1_in",
311
+ "past_key_2_in",
312
+ "past_value_2_in",
313
+ "past_key_3_in",
314
+ "past_value_3_in",
315
+ "past_key_4_in",
316
+ "past_value_4_in",
317
+ "past_key_5_in",
318
+ "past_value_5_in",
319
+ "past_key_6_in",
320
+ "past_value_6_in",
321
+ "past_key_7_in",
322
+ "past_value_7_in",
323
+ "past_key_8_in",
324
+ "past_value_8_in",
325
+ "past_key_9_in",
326
+ "past_value_9_in",
327
+ "past_key_10_in",
328
+ "past_value_10_in",
329
+ "past_key_11_in",
330
+ "past_value_11_in",
331
+ "past_key_12_in",
332
+ "past_value_12_in",
333
+ "past_key_13_in",
334
+ "past_value_13_in"
335
+ ],
336
+ "outputs": [
337
+ "_model_model_layers_13_Add_1_output_0",
338
+ "past_key_0_out",
339
+ "past_value_0_out",
340
+ "past_key_1_out",
341
+ "past_value_1_out",
342
+ "past_key_2_out",
343
+ "past_value_2_out",
344
+ "past_key_3_out",
345
+ "past_value_3_out",
346
+ "past_key_4_out",
347
+ "past_value_4_out",
348
+ "past_key_5_out",
349
+ "past_value_5_out",
350
+ "past_key_6_out",
351
+ "past_value_6_out",
352
+ "past_key_7_out",
353
+ "past_value_7_out",
354
+ "past_key_8_out",
355
+ "past_value_8_out",
356
+ "past_key_9_out",
357
+ "past_value_9_out",
358
+ "past_key_10_out",
359
+ "past_value_10_out",
360
+ "past_key_11_out",
361
+ "past_value_11_out",
362
+ "past_key_12_out",
363
+ "past_value_12_out",
364
+ "past_key_13_out",
365
+ "past_value_13_out"
366
+ ],
367
+ "session_options": {
368
+ "log_id": "onnxruntime-genai.tg2",
369
+ "provider_options": [
370
+ {
371
+ "qnn": {
372
+ "backend_path": "QnnHtp.dll",
373
+ "htp_performance_mode": "burst",
374
+ "enable_htp_shared_memory_allocator": "1",
375
+ "qnn_context_priority": "high"
376
+ }
377
+ }
378
+ ]
379
+ },
380
+ "run_on_prompt": false
381
+ },
382
+ "token-generator-3": {
383
+ "filename": "token_3_of_3_qnn_ctx.onnx",
384
+ "inputs": [
385
+ "_model_model_layers_13_Add_1_output_0",
386
+ "attention_mask",
387
+ "position_ids_cos",
388
+ "position_ids_sin",
389
+ "past_key_14_in",
390
+ "past_value_14_in",
391
+ "past_key_15_in",
392
+ "past_value_15_in",
393
+ "past_key_16_in",
394
+ "past_value_16_in",
395
+ "past_key_17_in",
396
+ "past_value_17_in",
397
+ "past_key_18_in",
398
+ "past_value_18_in",
399
+ "past_key_19_in",
400
+ "past_value_19_in",
401
+ "past_key_20_in",
402
+ "past_value_20_in",
403
+ "past_key_21_in",
404
+ "past_value_21_in",
405
+ "past_key_22_in",
406
+ "past_value_22_in",
407
+ "past_key_23_in",
408
+ "past_value_23_in",
409
+ "past_key_24_in",
410
+ "past_value_24_in",
411
+ "past_key_25_in",
412
+ "past_value_25_in",
413
+ "past_key_26_in",
414
+ "past_value_26_in",
415
+ "past_key_27_in",
416
+ "past_value_27_in"
417
+ ],
418
+ "outputs": [
419
+ "logits",
420
+ "past_key_14_out",
421
+ "past_value_14_out",
422
+ "past_key_15_out",
423
+ "past_value_15_out",
424
+ "past_key_16_out",
425
+ "past_value_16_out",
426
+ "past_key_17_out",
427
+ "past_value_17_out",
428
+ "past_key_18_out",
429
+ "past_value_18_out",
430
+ "past_key_19_out",
431
+ "past_value_19_out",
432
+ "past_key_20_out",
433
+ "past_value_20_out",
434
+ "past_key_21_out",
435
+ "past_value_21_out",
436
+ "past_key_22_out",
437
+ "past_value_22_out",
438
+ "past_key_23_out",
439
+ "past_value_23_out",
440
+ "past_key_24_out",
441
+ "past_value_24_out",
442
+ "past_key_25_out",
443
+ "past_value_25_out",
444
+ "past_key_26_out",
445
+ "past_value_26_out",
446
+ "past_key_27_out",
447
+ "past_value_27_out"
448
+ ],
449
+ "session_options": {
450
+ "log_id": "onnxruntime-genai.tg3",
451
+ "provider_options": [
452
+ {
453
+ "qnn": {
454
+ "backend_path": "QnnHtp.dll",
455
+ "htp_performance_mode": "burst",
456
+ "enable_htp_shared_memory_allocator": "1",
457
+ "qnn_context_priority": "high"
458
+ }
459
+ }
460
+ ]
461
+ },
462
+ "run_on_prompt": false
463
+ },
464
+ "dequantizer": {
465
+ "filename": "dequantizer.onnx",
466
+ "inputs": [
467
+ "logits"
468
+ ],
469
+ "outputs": [
470
+ "logits_dequantized"
471
+ ],
472
+ "session_options": {
473
+ "log_id": "onnxruntime-genai.dequantizer",
474
+ "provider_options": [
475
+ {}
476
+ ]
477
+ }
478
+ }
479
+ }
480
+ ]
481
+ },
482
+ "eos_token_id": [
483
+ 128001,
484
+ 128008,
485
+ 128009
486
+ ],
487
+ "pad_token_id": 128001,
488
+ "type": "decoder-pipeline",
489
+ "vocab_size": 128256
490
+ },
491
+ "search": {
492
+ "diversity_penalty": 0.0,
493
+ "do_sample": true,
494
+ "early_stopping": true,
495
+ "length_penalty": 1.0,
496
+ "max_length": 131072,
497
+ "min_length": 0,
498
+ "no_repeat_ngram_size": 0,
499
+ "num_beams": 1,
500
+ "num_return_sequences": 1,
501
+ "past_present_share_buffer": true,
502
+ "repetition_penalty": 1.0,
503
+ "temperature": 0.6,
504
+ "top_k": 1,
505
+ "top_p": 0.9
506
+ }
507
+ }
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+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "tokenizer_class": "PreTrainedTokenizerFast"
2062
+ }