diff --git a/examples/2.torch+state/torch+state.ipynb b/examples/2.torch+state/torch+state.ipynb index 4383cbf..bbe70d3 100644 --- a/examples/2.torch+state/torch+state.ipynb +++ b/examples/2.torch+state/torch+state.ipynb @@ -116,28 +116,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jernkun/Desktop/zk-stats-lib/zkstats/computation.py:249: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n", - " is_precise_aggregated = torch.tensor(1.0)\n", - "/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py:2174: FutureWarning: 'torch.onnx.symbolic_opset9._cast_Bool' is deprecated in version 2.0 and will be removed in the future. Please Avoid using this function and create a Cast node instead.\n", - " return fn(g, to_cast_func(g, input, False), to_cast_func(g, other, False))\n", - "/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/utils.py:1703: UserWarning: The exported ONNX model failed ONNX shape inference. The model will not be executable by the ONNX Runtime. If this is unintended and you believe there is a bug, please report an issue at https://github.com/pytorch/pytorch/issues. Error reported by strict ONNX shape inference: [ShapeInferenceError] (op_type:Where, node name: /Where_10): Y has inconsistent type tensor(float) (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/serialization/export.cpp:1490.)\n", - " _C._check_onnx_proto(proto)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==== Generate & Calibrate Setting ====\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -159,8 +140,9 @@ "name": "stdout", "output_type": "stream", "text": [ + "==== Generate & Calibrate Setting ====\n", "scale: [2]\n", - "setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":2,\"param_scale\":2,\"scale_rebase_multiplier\":10,\"lookup_range\":[-56,64],\"logrows\":12,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\",\"div_rebasing\":false,\"rebase_frac_zero_constants\":false,\"check_mode\":\"UNSAFE\"},\"num_rows\":2624,\"total_assignments\":812,\"total_const_size\":168,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,2],\"model_input_scales\":[2,2],\"module_sizes\":{\"kzg\":[],\"poseidon\":[2624,[2]]},\"required_lookups\":[{\"GreaterThan\":{\"a\":0.0}},\"ReLU\",\"Abs\",{\"Floor\":{\"scale\":8.0}}],\"required_range_checks\":[],\"check_mode\":\"UNSAFE\",\"version\":\"9.1.0\",\"num_blinding_factors\":null,\"timestamp\":1715672095084}\n" + "setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":2,\"param_scale\":2,\"scale_rebase_multiplier\":10,\"lookup_range\":[-56,64],\"logrows\":12,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Fixed\",\"div_rebasing\":false,\"rebase_frac_zero_constants\":false,\"check_mode\":\"UNSAFE\"},\"num_rows\":2624,\"total_assignments\":686,\"total_const_size\":303,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,2],\"model_input_scales\":[2,2],\"module_sizes\":{\"kzg\":[],\"poseidon\":[2624,[2]]},\"required_lookups\":[{\"GreaterThan\":{\"a\":0.0}},\"Abs\",\"ReLU\",{\"Floor\":{\"scale\":8.0}}],\"required_range_checks\":[],\"check_mode\":\"UNSAFE\",\"version\":\"9.1.0\",\"num_blinding_factors\":null,\"timestamp\":1717511682308}\n" ] } ], @@ -171,7 +153,7 @@ "def computation(state: State, x: list[torch.Tensor]):\n", " out_0 = torch.sum(x[0])\n", " out_1 = state.median(x[1])\n", - " return state.mean(torch.cat((out_0.unsqueeze(0), out_1.unsqueeze(0))).reshape(1,-1,1))\n", + " return state.mean(torch.cat((out_0.unsqueeze(0), out_1.unsqueeze(0))).reshape(-1,1))\n", "\n", "error = 0.01\n", "\n", @@ -190,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -218,14 +200,14 @@ "output_type": "stream", "text": [ "==== setting up ezkl ====\n", - "Time setup: 0.6220359802246094 seconds\n", + "Time setup: 0.5458400249481201 seconds\n", "=======================================\n", "==== Generating Witness ====\n", "witness boolean: 1.0\n", "witness result 1 : 12.5\n", "==== Generating Proof ====\n", - "proof: {'instances': [['cde936180fb7e379a578309232773e02b017d59f9001712b917a148b525d7b19', 'a38c8628cd223f38f854eade2722b8dd09b5797a0408398dd3d5160b6584e90b', '0100000000000000000000000000000000000000000000000000000000000000', '3200000000000000000000000000000000000000000000000000000000000000']], 'proof': 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'transcript_type': 'EVM'}\n", - "Time gen prf: 0.7742421627044678 seconds\n" + "proof: {'instances': [['cde936180fb7e379a578309232773e02b017d59f9001712b917a148b525d7b19', 'a38c8628cd223f38f854eade2722b8dd09b5797a0408398dd3d5160b6584e90b', '0100000000000000000000000000000000000000000000000000000000000000', '3200000000000000000000000000000000000000000000000000000000000000']], 'proof': 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', 'transcript_type': 'EVM'}\n", + "Time gen prf: 0.7617218494415283 seconds\n" ] } ], @@ -242,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -251,7 +233,7 @@ "[12.5]" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/3.state/state.ipynb b/examples/3.state/state.ipynb index 45ccb5d..222f384 100644 --- a/examples/3.state/state.ipynb +++ b/examples/3.state/state.ipynb @@ -123,7 +123,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jernkun/Desktop/zk-stats-lib/zkstats/computation.py:249: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n", + "/Users/jernkun/Desktop/zk-stats-lib/zkstats/computation.py:257: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n", " is_precise_aggregated = torch.tensor(1.0)\n", "/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py:2174: FutureWarning: 'torch.onnx.symbolic_opset9._cast_Bool' is deprecated in version 2.0 and will be removed in the future. Please Avoid using this function and create a Cast node instead.\n", " return fn(g, to_cast_func(g, input, False), to_cast_func(g, other, False))\n", @@ -160,7 +160,7 @@ "output_type": "stream", "text": [ "scale: [2]\n", - "setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":2,\"param_scale\":2,\"scale_rebase_multiplier\":10,\"lookup_range\":[-56,64],\"logrows\":12,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\",\"div_rebasing\":false,\"rebase_frac_zero_constants\":false,\"check_mode\":\"UNSAFE\"},\"num_rows\":2624,\"total_assignments\":1316,\"total_const_size\":281,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,2],\"model_input_scales\":[2,2],\"module_sizes\":{\"kzg\":[],\"poseidon\":[2624,[2]]},\"required_lookups\":[{\"Floor\":{\"scale\":8.0}},{\"GreaterThan\":{\"a\":0.0}},\"ReLU\",\"Abs\"],\"required_range_checks\":[],\"check_mode\":\"UNSAFE\",\"version\":\"9.1.0\",\"num_blinding_factors\":null,\"timestamp\":1715672107649}\n" + "setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":2,\"param_scale\":2,\"scale_rebase_multiplier\":10,\"lookup_range\":[-56,64],\"logrows\":12,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Fixed\",\"div_rebasing\":false,\"rebase_frac_zero_constants\":false,\"check_mode\":\"UNSAFE\"},\"num_rows\":2624,\"total_assignments\":1111,\"total_const_size\":496,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,2],\"model_input_scales\":[2,2],\"module_sizes\":{\"kzg\":[],\"poseidon\":[2624,[2]]},\"required_lookups\":[\"ReLU\",{\"Floor\":{\"scale\":8.0}},\"Abs\",{\"GreaterThan\":{\"a\":0.0}}],\"required_range_checks\":[],\"check_mode\":\"UNSAFE\",\"version\":\"9.1.0\",\"num_blinding_factors\":null,\"timestamp\":1717511715747}\n" ] } ], @@ -171,7 +171,7 @@ "def computation(state: State, x: list[torch.Tensor]):\n", " out_0 = state.median(x[0])\n", " out_1 = state.median(x[1])\n", - " return state.mean(torch.cat((out_0.unsqueeze(0), out_1.unsqueeze(0))).reshape(1,-1,1))\n", + " return state.mean(torch.cat((out_0.unsqueeze(0), out_1.unsqueeze(0))).reshape(-1,1))\n", "\n", "error = 0.01\n", "\n", @@ -218,14 +218,14 @@ "output_type": "stream", "text": [ "==== setting up ezkl ====\n", - "Time setup: 0.5770092010498047 seconds\n", + "Time setup: 0.5848329067230225 seconds\n", "=======================================\n", "==== Generating Witness ====\n", "witness boolean: 1.0\n", "witness result 1 : 3.25\n", "==== Generating Proof ====\n", - "proof: {'instances': [['cde936180fb7e379a578309232773e02b017d59f9001712b917a148b525d7b19', 'a38c8628cd223f38f854eade2722b8dd09b5797a0408398dd3d5160b6584e90b', '0100000000000000000000000000000000000000000000000000000000000000', '0d00000000000000000000000000000000000000000000000000000000000000']], 'proof': 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', 'transcript_type': 'EVM'}\n", - "Time gen prf: 0.7872781753540039 seconds\n" + "proof: {'instances': [['cde936180fb7e379a578309232773e02b017d59f9001712b917a148b525d7b19', 'a38c8628cd223f38f854eade2722b8dd09b5797a0408398dd3d5160b6584e90b', '0100000000000000000000000000000000000000000000000000000000000000', '0d00000000000000000000000000000000000000000000000000000000000000']], 'proof': '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', 'transcript_type': 'EVM'}\n", + "Time gen prf: 0.7657780647277832 seconds\n" ] } ], diff --git a/playground/README.md b/playground/README.md new file mode 100644 index 0000000..9711b3e --- /dev/null +++ b/playground/README.md @@ -0,0 +1 @@ +Nothing much useful here. Just a playground to test codes real quick. diff --git a/playground/example.onnx b/playground/example.onnx new file mode 100644 index 0000000..6e01e10 Binary files /dev/null and b/playground/example.onnx differ diff --git a/playground/playground.ipynb b/playground/playground.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/playground.py b/playground/playground.py similarity index 100% rename from playground.py rename to playground/playground.py diff --git a/tests/test_computation.py b/tests/test_computation.py index 3871942..aa23693 100644 --- a/tests/test_computation.py +++ b/tests/test_computation.py @@ -39,7 +39,7 @@ def nested_computation(state: State, args: list[torch.Tensor]): out_8 = state.covariance(x, y) out_9 = state.correlation(y, z) out_10 = state.linear_regression(x, y) - slope, intercept = out_10[0][0][0], out_10[0][1][0] + slope, intercept = out_10[0][0], out_10[1][0] reshaped = torch.cat(( out_0.unsqueeze(0), out_1.unsqueeze(0), @@ -53,7 +53,7 @@ def nested_computation(state: State, args: list[torch.Tensor]): out_9.unsqueeze(0), slope.unsqueeze(0), intercept.unsqueeze(0), - )).reshape(1,-1,1) + )).reshape(-1,1) out_10 = state.mean(reshaped) return out_10 @@ -125,9 +125,9 @@ def test_nested_computation(tmp_path, column_0: torch.Tensor, column_1: torch.Te op_10 = ops[10] assert isinstance(op_10, Regression) out_10 = statistics.linear_regression(x.tolist(), y.tolist()) - assert op_10.result.shape == (1, 2, 1) - assert_result(op_10.result[0][0][0], out_10.slope) - assert_result(op_10.result[0][1][0], out_10.intercept) + assert op_10.result.shape == ( 2, 1) + assert_result(op_10.result[0][0], out_10.slope) + assert_result(op_10.result[1][0], out_10.intercept) op_11 = ops[11] assert isinstance(op_11, Mean) diff --git a/tests/test_ops.py b/tests/test_ops.py index 5a47390..e014068 100644 --- a/tests/test_ops.py +++ b/tests/test_ops.py @@ -51,10 +51,10 @@ def test_linear_regression(tmp_path, column_0: torch.Tensor, column_1: torch.Ten expected_res = statistics.linear_regression(column_0.tolist(), column_1.tolist()) columns = [column_0, column_1] regression = Regression.create(columns, error) - # shape = [1, 2, 1] + # shape = [2, 1] actual_res = regression.result - assert_result(expected_res.slope, actual_res[0][0][0]) - assert_result(expected_res.intercept, actual_res[0][1][0]) + assert_result(expected_res.slope, actual_res[0][0]) + assert_result(expected_res.intercept, actual_res[1][0]) class Model(IModel): def forward(self, *x: list[torch.Tensor]) -> tuple[IsResultPrecise, torch.Tensor]: return regression.ezkl(x), regression.result diff --git a/zkstats/computation.py b/zkstats/computation.py index 879ddbb..bd25ba2 100644 --- a/zkstats/computation.py +++ b/zkstats/computation.py @@ -196,7 +196,7 @@ def _call_op(self, x: list[torch.Tensor], op_type: Type[Operation]) -> Union[tor self.op_dict['Correlation']+=1 elif isinstance(op, Regression): result_array = [] - for ele in op.result.data[0]: + for ele in op.result.data: result_array.append(ele[0].item()) if 'Regression' not in self.op_dict: self.precal_witness['Regression_0'] = [result_array] @@ -204,6 +204,14 @@ def _call_op(self, x: list[torch.Tensor], op_type: Type[Operation]) -> Union[tor else: self.precal_witness['Regression_'+str(self.op_dict['Regression'])] = [result_array] self.op_dict['Regression']+=1 + # for ele in op.result.data[0]: + # result_array.append(ele[0].item()) + # if 'Regression' not in self.op_dict: + # self.precal_witness['Regression_0'] = [result_array] + # self.op_dict['Regression']=1 + # else: + # self.precal_witness['Regression_'+str(self.op_dict['Regression'])] = [result_array] + # self.op_dict['Regression']+=1 # for verifier else: # print('Verifier side create') @@ -252,13 +260,13 @@ def is_precise() -> IsResultPrecise: is_precise_aggregated = torch.logical_and(is_precise_aggregated, res) if self.isProver: json.dump(self.precal_witness, open(self.precal_witness_path, 'w')) - return is_precise_aggregated, op.result+(x[0]-x[0])[0][0][0] + return is_precise_aggregated, op.result+(x[0]-x[0])[0][0] elif current_op_index > len_ops - 1: # Sanity check that current op index does not exceed the length of ops raise Exception(f"current_op_index out of bound: {current_op_index=} > {len_ops=}") else: - return op.result+(x[0]-x[0])[0][0][0] + return op.result+(x[0]-x[0])[0][0] class IModel(nn.Module): @@ -302,7 +310,7 @@ def forward(self, *x: list[torch.Tensor]) -> tuple[IsResultPrecise, torch.Tensor # print('x sy: ') result = computation(state, x) if len(result) ==1: - return x[0][0][0][0]-x[0][0][0][0]+torch.tensor(1.0), result + return (x[0]-x[0])[0][0]+torch.tensor(1.0), result else: return result # print('state:: ', state.aggregate_witness_path) diff --git a/zkstats/core.py b/zkstats/core.py index 3007372..7cc2dc1 100644 --- a/zkstats/core.py +++ b/zkstats/core.py @@ -319,15 +319,15 @@ def _export_onnx(model: Type[IModel], data_tensor_array: list[torch.Tensor], mod # Flips the neural net into inference mode circuit.eval() input_names = [] - dynamic_axes = {} + # dynamic_axes = {} data_tensor_tuple = () for i in range(len(data_tensor_array)): data_tensor_tuple += (data_tensor_array[i],) input_index = "input"+str(i+1) input_names.append(input_index) - dynamic_axes[input_index] = {0 : 'batch_size'} - dynamic_axes["output"] = {0 : 'batch_size'} + # dynamic_axes[input_index] = {0 : 'batch_size'} + # dynamic_axes["output"] = {0 : 'batch_size'} # Export the model torch.onnx.export(circuit, # model being run @@ -338,7 +338,8 @@ def _export_onnx(model: Type[IModel], data_tensor_array: list[torch.Tensor], mod do_constant_folding=True, # whether to execute constant folding for optimization input_names = input_names, # the model's input names output_names = ['output'], # the model's output names - dynamic_axes=dynamic_axes) + # dynamic_axes=dynamic_axes + ) # mode is either "accuracy" or "resources" @@ -436,7 +437,7 @@ def _process_data( for col in col_array: data = data_onefile[col] data_tensor = torch.tensor(data, dtype = torch.float32) - data_tensor_array.append(torch.reshape(data_tensor, (1,-1,1))) + data_tensor_array.append(torch.reshape(data_tensor, (-1,1))) sel_data.append(data) # Serialize data into file: # sel_data comes from `data` diff --git a/zkstats/ops.py b/zkstats/ops.py index 1e11af4..cbdca2f 100644 --- a/zkstats/ops.py +++ b/zkstats/ops.py @@ -53,10 +53,10 @@ def ezkl(self, x: list[torch.Tensor]) -> IsResultPrecise: def to_1d(x: torch.Tensor) -> torch.Tensor: x_shape = x.size() - # Only allows 1d array or [1, len(x), 1] + # Only allows 1d array or [len(x), 1] if len(x_shape) == 1: return x - elif len(x_shape) == 3 and x_shape[0] == 1 and x_shape[2] == 1: + elif len(x_shape) == 2 and x_shape[1] == 1: return x.reshape(-1) else: raise Exception(f"Unsupported shape: {x_shape=}") @@ -97,7 +97,7 @@ def create(cls, x: list[torch.Tensor], error: float, precal_witness:Optional[di def ezkl(self, x: list[torch.Tensor]) -> IsResultPrecise: x = x[0] - old_size = x.size()[1] + old_size = x.size()[0] size = torch.sum(torch.where(x!=MagicNumber, 1.0, 0.0)) min_x = torch.min(x) x = torch.where(x==MagicNumber,min_x-1, x) @@ -141,7 +141,7 @@ def create(cls, x: list[torch.Tensor], error: float, precal_witness:Optional[di return cls(torch.tensor(precal_witness['GeometricMean_'+str(op_dict['GeometricMean'])][0]), error) def ezkl(self, x: list[torch.Tensor]) -> IsResultPrecise: - # Assume x is [1, n, 1] + # Assume x is [n, 1] x = x[0] size = torch.sum(torch.where(x!=MagicNumber, 1.0, 0.0)) x = torch.where(x==MagicNumber, 1.0, x) @@ -166,7 +166,7 @@ def create(cls, x: list[torch.Tensor], error: float, precal_witness:Optional[dic def ezkl(self, x: list[torch.Tensor]) -> IsResultPrecise: - # Assume x is [1, n, 1] + # Assume x is [n, 1] x = x[0] size = torch.sum(torch.where(x!=MagicNumber, 1.0, 0.0)) return torch.abs((self.result*torch.sum(torch.where(x==MagicNumber, 0.0, torch.div(1.0, x)))) - size)<=torch.abs(self.error*size) @@ -237,15 +237,15 @@ def create(cls, x: list[torch.Tensor], error: float, precal_witness:Optional[di def ezkl(self, x: list[torch.Tensor]) -> IsResultPrecise: - # Assume x is [1, n, 1] + # Assume x is [n, 1] x = x[0] min_x = torch.min(x) - old_size = x.size()[1] + old_size = x.size()[0] x = torch.where(x==MagicNumber, min_x-1, x) count_equal = torch.sum(torch.where(x==self.result, 1.0, 0.0)) count_check = 0 - for ele in x[0]: + for ele in x: bool1 = torch.sum(torch.where(x==ele[0], 1.0, 0.0))<=count_equal bool2 = ele[0]==min_x-1 count_check += torch.logical_or(bool1, bool2) @@ -538,16 +538,20 @@ def __init__(self, xs: list[torch.Tensor], y: torch.Tensor, error: float, preca x_one = stacked_x(x_1ds) result_1d = np.matmul(np.matmul(np.linalg.inv(np.matmul(x_one.transpose(), x_one)), x_one.transpose()), y_1d) - result = torch.tensor(result_1d, dtype = torch.float32).reshape(1, -1, 1) - # print('result: ', result) + # result = torch.tensor(result_1d, dtype = torch.float32).reshape(1, -1, 1) + result = torch.tensor(result_1d, dtype = torch.float32).reshape(-1,1) super().__init__(result, error) + # print('result regression: ', result) else: if op_dict is None: - result = torch.tensor(precal_witness['Regression_0']).reshape(1,-1,1) + # result = torch.tensor(precal_witness['Regression_0']).reshape(1,-1,1) + result = torch.tensor(precal_witness['Regression_0']).reshape(-1,1) elif 'Regression' not in op_dict: - result = torch.tensor(precal_witness['Regression_0']).reshape(1,-1,1) + # result = torch.tensor(precal_witness['Regression_0']).reshape(1,-1,1) + result = torch.tensor(precal_witness['Regression_0']).reshape(-1,1) else: - result = torch.tensor(precal_witness['Regression_'+str(op_dict['Regression'])]).reshape(1,-1,1) + # result = torch.tensor(precal_witness['Regression_'+str(op_dict['Regression'])]).reshape(1,-1,1) + result = torch.tensor(precal_witness['Regression_'+str(op_dict['Regression'])]).reshape(-1,1) # for ele in precal_witness['Regression']: # precal_witness_arr.append(torch.tensor(ele)) @@ -565,9 +569,9 @@ def ezkl(self, args: list[torch.Tensor]) -> IsResultPrecise: # infer y from the last parameter y = args[-1] y = torch.where(y==MagicNumber,0.0, y) - x_one = torch.cat((*args[:-1], torch.ones_like(args[0])), dim=2) - x_one = torch.where((x_one[:,:,0] ==MagicNumber).unsqueeze(-1), torch.tensor([0.0]*x_one.size()[2]), x_one) - x_t = torch.transpose(x_one, 1, 2) + x_one = torch.cat((*args[:-1], torch.ones_like(args[0])), dim = 1) + x_one = torch.where((x_one[:,0] ==MagicNumber).unsqueeze(-1), torch.tensor([0.0]*x_one.size()[1]), x_one) + x_t = torch.transpose(x_one, 0, 1) left = x_t @ x_one @ self.result - x_t @ y right = self.error*x_t @ y