Quantization aware training (QAT)

ossama_khalyl · August 24, 2023, 10:26am

this is My CNN model for face recognition :

class FaceRecognitionModel_QAT(nn.Module):
    #n_bits (number of bits for quantization).
    def __init__(self, num_classes, n_bits):
        super().__init__()
        a_bits = n_bits
        w_bits = n_bits
        #to quantize the input by placing it at the entry point of the network.
        # we have in this cnn 18179856 MAC
        self.q1 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)               
        self.conv1 = qnn.QuantConv2d(3, 32, 3, stride=1, padding=1, weight_bit_width=w_bits)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.q2 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)
        self.conv2 = qnn.QuantConv2d(32, 64, 3, stride=1, padding=1, weight_bit_width=w_bits)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.q3 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)
        self.conv3 = qnn.QuantConv2d(64, 128, 3, stride=1, padding=1, weight_bit_width=w_bits)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.q4 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)
        self.flatten = nn.Flatten()
        self.q5 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)
        self.fc1 = qnn.QuantLinear(128*16*16,512,bias=True,weight_bit_width=w_bits)# Assuming input image size is 128x128 after pooling
        self.q6 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)
        self.fc2 = qnn.QuantLinear(512,num_classes,bias=True,weight_bit_width=w_bits)
      # Enable pruning, prepared for training
        self.toggle_pruning(True)

     
    def toggle_pruning(self, enable):
        """Enables or removes pruning."""

        # Maximum number of active neurons (i.e., corresponding weight != 0)
        n_active = 12

        # Go through all the convolution layers
        for layer in (self.conv1, self.conv2, self.conv3):
            s = layer.weight.shape

            # Compute fan-in (number of inputs to a neuron)
            # and fan-out (number of neurons in the layer)
            st = [s[0], np.prod(s[1:])]

            # The number of input neurons (fan-in) is the product of
            # the kernel width x height x inChannels.
            if st[1] > n_active:
                if enable:
                    # This will create a forward hook to create a mask tensor that is multiplied
                    # with the weights during forward. The mask will contain 0s or 1s
                    prune.l1_unstructured(layer, "weight", (st[1] - n_active) * st[0])
                else:
                    # When disabling pruning, the mask is multiplied with the weights
                    # and the result is stored in the weights member
                    prune.remove(layer, "weight") 

    def forward(self, x):                           
        x = x.permute(0, 3, 1, 2)  # Transpose the dimensions to [batch_size, channels, height, width]
        x = self.q1(x)                            
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.q2(x)                            
        x = self.pool2(F.relu(self.conv2(x)))
        x = self.q3(x)
        x = self.pool3(F.relu(self.conv3(x)))
        x = self.q4(x)
        x = self.flatten(x)
        x = self.q5(x)
        x = F.relu(self.fc1(x))
        x = self.q6(x)
        x = self.fc2(x)
        return x

The first question is about max_pooling when i make QAT Does it have the same architecture as average pooling ?

The second question when i use the function compile_brevitas_qat_model after training i got this error “Unsupported: ONNX export of convolution for kernel of unknown shape.”

luis · August 25, 2023, 6:59am

Hello @ossama_khalyl,

For max-pooling you probably don’t need to add a QuantIdentity since the data is already quantized. But not sure if that answers your question.
We are able to reproduce an error using your code with pruning, and another one without pruning but none are the one you describe.
Could you please send us a full traceback so that we can look into it? Also a few things that you can explore to help us debug:
- move the data permutation out of the model
- remove pruning from your init

ossama_khalyl · August 27, 2023, 3:41pm

Thank you @luis for your response. I have relocated the data permutation outside of the model and removed the pruning from my CNN. Here is my updated CNN code:

class FaceRecognitionModel_QAT(nn.Module):
    #n_bits (number of bits for quantization).
    def __init__(self, num_classes, n_bits):
        super().__init__()
        a_bits = n_bits
        w_bits = n_bits

        #to quantize the input by placing it at the entry point of the network.
        # we have in this cnn 18179856 MAC
        self.q1 = qnn.QuantIdentity(bit_width=a_bits, return_quant_tensor=True)               
        self.conv1 = qnn.QuantConv2d(3, 32, 3, stride=1, padding=1, weight_bit_width=w_bits)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = qnn.QuantConv2d(32, 64, 3, stride=1, padding=1, weight_bit_width=w_bits)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = qnn.QuantConv2d(64, 128, 3, stride=1, padding=1, weight_bit_width=w_bits)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = qnn.QuantLinear(128*16*16,512,bias=False,weight_bit_width=w_bits)# Assuming input image size is 128x128 after pooling
        self.fc2 = qnn.QuantLinear(512,num_classes,bias=False,weight_bit_width=w_bits)
      # Enable pruning, prepared for training
        #self.toggle_pruning(True)

     
    """ def toggle_pruning(self, enable):
        

        # Maximum number of active neurons (i.e., corresponding weight != 0)
        n_active = 12

        # Go through all the convolution layers
        for layer in (self.conv1, self.conv2, self.conv3):
            s = layer.weight.shape

            # Compute fan-in (number of inputs to a neuron)
            # and fan-out (number of neurons in the layer)
            st = [s[0], np.prod(s[1:])]

            # The number of input neurons (fan-in) is the product of
            # the kernel width x height x inChannels.
            if st[1] > n_active:
                if enable:
                    # This will create a forward hook to create a mask tensor that is multiplied
                    # with the weights during forward. The mask will contain 0s or 1s
                    prune.l1_unstructured(layer, "weight", (st[1] - n_active) * st[0])
                else:
                    # When disabling pruning, the mask is multiplied with the weights
                    # and the result is stored in the weights member
                    prune.remove(layer, "weight") """

    def forward(self, x):                           
        x = self.q1(x)                            
        x = self.pool1(F.relu(self.conv1(x)))                     
        x = self.pool2(F.relu(self.conv2(x)))
        x = self.pool3(F.relu(self.conv3(x)))
        x = self.flatten(x)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

The complete error message is:

[W shape_type_inference.cpp:1920] Warning: The shape inference of onnx.brevitas::Quant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (function UpdateReliable)
[W shape_type_inference.cpp:1920] Warning: The shape inference of onnx.brevitas::Quant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (function UpdateReliable)
================ Diagnostic Run torch.onnx.export version 2.0.1 ================
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 2 WARNING 0 ERROR ========================
2 WARNING were not printed due to the log level.

---------------------------------------------------------------------------
SymbolicValueError                        Traceback (most recent call last)
Cell In[16], line 5
      3 sim_time = []
      4 for idx in range(len(bit_range)):
----> 5     q_module = compile_brevitas_qat_model(nets[idx], x_train_modified)
      7     accum_bits.append(q_module.fhe_circuit.graph.maximum_integer_bit_width())
      9     start_time = time.time()

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/concrete/ml/torch/compile.py:415, in compile_brevitas_qat_model(torch_model, torch_inputset, n_bits, configuration, artifacts, show_mlir, rounding_threshold_bits, p_error, global_p_error, output_onnx_file, verbose)
    413 exporter.onnx_passes.append("eliminate_nop_pad")
    414 exporter.onnx_passes.append("fuse_pad_into_conv")
--> 415 onnx_model = exporter.export(
    416     torch_model,
    417     args=dummy_input_for_tracing,
    418     export_path=str(output_onnx_file_path),
    419     keep_initializers_as_inputs=False,
    420     opset_version=OPSET_VERSION_FOR_ONNX_EXPORT,
    421 )
    422 onnx_model = remove_initializer_from_input(onnx_model)
    424 if n_bits is None:

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/manager.py:159, in ONNXBaseManager.export(cls, module, args, export_path, input_shape, input_t, disable_warnings, **onnx_export_kwargs)
    149 @classmethod
    150 def export(
    151         cls,
   (...)
    157         disable_warnings=True,
    158         **onnx_export_kwargs):
--> 159     return cls.export_onnx(module, args, export_path, input_shape, input_t, disable_warnings, **onnx_export_kwargs)

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/manager.py:131, in ONNXBaseManager.export_onnx(cls, module, args, export_path, input_shape, input_t, disable_warnings, **onnx_export_kwargs)
    129         model_bytes = BytesIO()
    130         export_target = model_bytes
--> 131     torch.onnx.export(module, args, export_target, **onnx_export_kwargs)
    133 # restore the model to previous properties
    134 module.apply(lambda m: _restore_inp_caching_mode(m))

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py:506, in export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions)
    188 @_beartype.beartype
    189 def export(
    190     model: Union[torch.nn.Module, torch.jit.ScriptModule, torch.jit.ScriptFunction],
   (...)
    206     export_modules_as_functions: Union[bool, Collection[Type[torch.nn.Module]]] = False,
    207 ) -> None:
    208     r"""Exports a model into ONNX format.
    209 
    210     If ``model`` is not a :class:`torch.jit.ScriptModule` nor a
   (...)
    503             All errors are subclasses of :class:`errors.OnnxExporterError`.
    504     """
--> 506     _export(
    507         model,
    508         args,
    509         f,
    510         export_params,
    511         verbose,
    512         training,
    513         input_names,
    514         output_names,
    515         operator_export_type=operator_export_type,
    516         opset_version=opset_version,
    517         do_constant_folding=do_constant_folding,
    518         dynamic_axes=dynamic_axes,
    519         keep_initializers_as_inputs=keep_initializers_as_inputs,
    520         custom_opsets=custom_opsets,
    521         export_modules_as_functions=export_modules_as_functions,
    522     )

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py:1548, in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, onnx_shape_inference, export_modules_as_functions)
   1545     dynamic_axes = {}
   1546 _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
-> 1548 graph, params_dict, torch_out = _model_to_graph(
   1549     model,
   1550     args,
   1551     verbose,
   1552     input_names,
   1553     output_names,
   1554     operator_export_type,
   1555     val_do_constant_folding,
   1556     fixed_batch_size=fixed_batch_size,
   1557     training=training,
   1558     dynamic_axes=dynamic_axes,
   1559 )
   1561 # TODO: Don't allocate a in-memory string for the protobuf
   1562 defer_weight_export = (
   1563     export_type is not _exporter_states.ExportTypes.PROTOBUF_FILE
   1564 )

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py:1117, in _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)
   1114 params_dict = _get_named_param_dict(graph, params)
   1116 try:
-> 1117     graph = _optimize_graph(
   1118         graph,
   1119         operator_export_type,
   1120         _disable_torch_constant_prop=_disable_torch_constant_prop,
   1121         fixed_batch_size=fixed_batch_size,
   1122         params_dict=params_dict,
   1123         dynamic_axes=dynamic_axes,
   1124         input_names=input_names,
   1125         module=module,
   1126     )
   1127 except Exception as e:
   1128     torch.onnx.log("Torch IR graph at exception: ", graph)

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py:665, in _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)
    662     _C._jit_pass_onnx_set_dynamic_input_shape(graph, dynamic_axes, input_names)
    663 _C._jit_pass_onnx_lint(graph)
--> 665 graph = _C._jit_pass_onnx(graph, operator_export_type)
    666 _C._jit_pass_onnx_lint(graph)
    667 _C._jit_pass_lint(graph)

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py:1891, in _run_symbolic_function(graph, block, node, inputs, env, operator_export_type)
   1886     if symbolic_fn is not None:
   1887         # TODO Wrap almost identical attrs assignment or comment the difference.
   1888         attrs = {
   1889             k: symbolic_helper._node_get(node, k) for k in node.attributeNames()
   1890         }
-> 1891         return symbolic_fn(graph_context, *inputs, **attrs)
   1893 attrs = {
   1894     k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
   1895     for k in node.attributeNames()
   1896 }
   1897 if namespace == "onnx":
   1898     # Clone node to trigger ONNX shape inference

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:306, in parse_args.<locals>.decorator.<locals>.wrapper(g, *args, **kwargs)
    300 if len(kwargs) == 1:
    301     assert "_outputs" in kwargs, (
    302         f"Symbolic function {fn.__name__}'s '**kwargs' can only contain "
    303         f"'_outputs' key at '**kwargs'. "
    304         f"{FILE_BUG_MSG}"
    305     )
--> 306 return fn(g, *args, **kwargs)

File ~/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py:2451, in _convolution(g, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32)
   2448     kernel_shape = None
   2450 if kernel_shape is None or any([i is None for i in kernel_shape]):
-> 2451     raise errors.SymbolicValueError(
   2452         "Unsupported: ONNX export of convolution for kernel of unknown shape.",
   2453         input,
   2454     )
   2456 args = [input, weight]
   2457 # ONNX only supports 1D bias

SymbolicValueError: Unsupported: ONNX export of convolution for kernel of unknown shape.  [Caused by the value 'x defined in (%x : Float(*, *, *, *, strides=[49152, 1, 384, 3], requires_grad=0, device=cpu) = onnx.brevitas::Quant[narrow=0, rounding_mode="ROUND", signed=1](%x.193, %scale, %zero_point, %bit_width), scope: __main__.FaceRecognitionModel_QAT::/brevitas.nn.quant_activation.QuantIdentity::q1/brevitas.proxy.runtime_quant.ActQuantProxyFromInjector::act_quant/brevitas.export.onnx.qonnx.handler.BrevitasActQuantProxyHandler::export_handler # /home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/autograd/function.py:506:0
)' (type 'Tensor') in the TorchScript graph. The containing node has kind 'onnx.brevitas::Quant'.] 
    (node defined in /home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/autograd/function.py(506): apply
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/qonnx/handler.py(49): symbolic_execution
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/handler.py(114): forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/proxy/runtime_quant.py(150): forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/nn/quant_layer.py(148): forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1501): _call_impl
/tmp/ipykernel_393/1994898414.py(51): forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/jit/_trace.py(118): wrapper
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/jit/_trace.py(127): forward
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py(1548): _export
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/onnx/utils.py(506): export
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/manager.py(131): export_onnx
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/manager.py(159): export
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/concrete/ml/torch/compile.py(415): compile_brevitas_qat_model
/tmp/ipykernel_393/382210858.py(5): <module>
/home/khalyl/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py(3508): run_code
/home/khalyl/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py(3448): run_ast_nodes
/home/khalyl/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py(3269): run_cell_async
/home/khalyl/.local/lib/python3.10/site-packages/IPython/core/async_helpers.py(129): _pseudo_sync_runner
/home/khalyl/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py(3064): _run_cell
/home/khalyl/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py(3009): run_cell
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/zmqshell.py(546): run_cell
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/ipkernel.py(422): do_execute
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py(740): execute_request
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py(412): dispatch_shell
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py(505): process_one
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py(516): dispatch_queue
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/asyncio/events.py(80): _run
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/asyncio/base_events.py(1906): _run_once
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/asyncio/base_events.py(603): run_forever
/home/khalyl/.local/lib/python3.10/site-packages/tornado/platform/asyncio.py(195): start
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel/kernelapp.py(736): start
/home/khalyl/.local/lib/python3.10/site-packages/traitlets/config/application.py(1043): launch_instance
/home/khalyl/.local/lib/python3.10/site-packages/ipykernel_launcher.py(17): <module>
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/runpy.py(86): _run_code
/home/khalyl/anaconda3/envs/myenv/lib/python3.10/runpy.py(196): _run_module_as_main
)

    Inputs:
        #0: x.193 defined in (%x.193 : Float(1, 3, 128, 128, strides=[49152, 1, 384, 3], requires_grad=0, device=cpu), %conv1.weight : Float(32, 3, 3, 3, strides=[27, 9, 3, 1], requires_grad=1, device=cpu), %conv1.bias : Float(32, strides=[1], requires_grad=1, device=cpu), %conv2.weight : Float(64, 32, 3, 3, strides=[288, 9, 3, 1], requires_grad=1, device=cpu), %conv2.bias : Float(64, strides=[1], requires_grad=1, device=cpu), %conv3.weight : Float(128, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=1, device=cpu), %conv3.bias : Float(128, strides=[1], requires_grad=1, device=cpu), %fc1.weight : Float(512, 32768, strides=[32768, 1], requires_grad=1, device=cpu), %fc2.weight : Float(158, 512, strides=[512, 1], requires_grad=1, device=cpu) = prim::Param()
    )  (type 'Tensor')
        #1: scale defined in (%scale : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.5}](), scope: __main__.FaceRecognitionModel_QAT::/brevitas.nn.quant_activation.QuantIdentity::q1/brevitas.proxy.runtime_quant.ActQuantProxyFromInjector::act_quant/brevitas.export.onnx.qonnx.handler.BrevitasActQuantProxyHandler::export_handler # /home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/autograd/function.py:506:0
    )  (type 'Tensor')
        #2: zero_point defined in (%zero_point : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0}](), scope: __main__.FaceRecognitionModel_QAT::/brevitas.nn.quant_activation.QuantIdentity::q1/brevitas.proxy.runtime_quant.ActQuantProxyFromInjector::act_quant/brevitas.export.onnx.qonnx.handler.BrevitasActQuantProxyHandler::export_handler # /home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/autograd/function.py:506:0
    )  (type 'Tensor')
        #3: bit_width defined in (%bit_width : Float(requires_grad=0, device=cpu) = onnx::Constant[value={2}](), scope: __main__.FaceRecognitionModel_QAT::/brevitas.nn.quant_activation.QuantIdentity::q1/brevitas.proxy.runtime_quant.ActQuantProxyFromInjector::act_quant/brevitas.export.onnx.qonnx.handler.BrevitasActQuantProxyHandler::export_handler # /home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/brevitas/export/onnx/qonnx/handler.py:46:0
    )  (type 'Tensor')
    Outputs:
        #0: x defined in (%x : Float(*, *, *, *, strides=[49152, 1, 384, 3], requires_grad=0, device=cpu) = onnx.brevitas::Quant[narrow=0, rounding_mode="ROUND", signed=1](%x.193, %scale, %zero_point, %bit_width), scope: __main__.FaceRecognitionModel_QAT::/brevitas.nn.quant_activation.QuantIdentity::q1/brevitas.proxy.runtime_quant.ActQuantProxyFromInjector::act_quant/brevitas.export.onnx.qonnx.handler.BrevitasActQuantProxyHandler::export_handler # /home/khalyl/anaconda3/envs/myenv/lib/python3.10/site-packages/torch/autograd/function.py:506:0
    )  (type 'Tensor')

ossama_khalyl · September 3, 2023, 11:24pm

can someone answer me or give mz a solution for this error ?

luis · September 4, 2023, 9:09am

Hello @ossama_khalyl , sorry for the delay.

We weren’t able to replicate the exact issue you are having. Could you please link here the code that you are using for compilation and the list of versions of packages in your python environnment (mainly concrete-ml, concrete-python, torch, onnx and brevitas)