Quantization folding pass #7240
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/7240
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digantdesai
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backends/arm/operators/op_max.py
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| output.shape = tosa_shape(output.shape, output.dim_order) | ||
| min_output = tosa_graph.addIntermediate(output.shape, ts.DType.INT32) |
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| min_output = tosa_graph.addIntermediate(output.shape, ts.DType.INT32) | |
| max_output = tosa_graph.addIntermediate(output.shape, ts.DType.INT32) |
backends/arm/operators/op_max.py
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| x_scale = input_qparams[0].scale | ||
| x_zp = input_qparams[0].zp | ||
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| y_scale = input_qparams[1].scale | ||
| y_zp = input_qparams[1].zp | ||
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| assert ( | ||
| x_zp == y_zp | ||
| ), "Different zp for inputs, MAX should be quantized with shared quantization!" | ||
| assert ( | ||
| x_scale == y_scale | ||
| ), "Different scale for input, MAX should be quantized with shared quantization!" |
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refactor this as a util to assert shared qconfigs across inputs?
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| class SimpleQuantizeModel(torch.nn.Module): | ||
| def forward(self, x): | ||
| return x + x |
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nit: may be make it slightly more complicated with >1 input tensors and >1 add-nodes? may be like max((x + x), (y + y))
Also and also chain of nodes i.e. q0->dq0->op1->q2->dq2->op2->q3-dq3 => q0->op1*->op2*->dq3
backends/arm/tosa_quant_utils.py
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| dim_order = tensor.dim_order | ||
| tensor.shape = [tensor.shape[i] for i in dim_order] | ||
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| qargs = list(cast(dict[int, QuantArgs], node.meta["input_qparams"]).values()) |
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Assert input_qparams in node.meta
backends/arm/tosa_quant_utils.py
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| """ | ||
| assert len(node.meta["output_qparams"]) == 1 | ||
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| qargs_out = cast(dict[int, QuantArgs], node.meta["output_qparams"])[0] |
backends/arm/tosa_quant_utils.py
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| return rescaled_nodes, min_scale | ||
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| def insert_rescale_node_back_to_int8( |
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| def insert_rescale_node_back_to_int8( | |
| def insert_rescale_node_to_int8( |
Reuse the logic from the node visiting quantization handling, but replace the quantization parameter fetching from the node meta values. Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I9a7bbf6384284e60118756ec5661f6b11847aba7
Fold DQ/Q nodes into the target operators specified to the pass. Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I8a09dc0b887dd5f3915ca157f578ecf51772a1a2
Uses the fold DQ/Q pass to encapsulate the quantization information within the node. Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I3adbab7e2a23a0208a03bbc423b38c15221a4959
Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I9230209ed3d6cc0b5ec7a35512248648bb8380ee
Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I6154e13a5a6b75549862709d632ee6dd5c8b0e7f
Adds a helper function to retrieve QuantArgs from node.meta and cleanup the handling a bit by introducing the __eq__ operator for QuantArgs. Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I519a9a286a36a278f40ffb6c679192a54d9f940d
Signed-off-by: Per Åstrand <per.astrand@arm.com> Change-Id: I2d133f4347d9999c770e5337162c222368c212f2
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pull / unittest / macos / macos-job (pull_request) failing seems to be unrelated (test_flamingo_vision_encoder) |
digantdesai
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Summary
Adds a folding pass to fold in q and dq nodes.
Test plan
Added test for the new pass