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@calad0i calad0i commented Jul 21, 2025

Description

Extends full model bit propagation infra to other frontends; config_from_whatever_model() may still be needed.

Type of change

  • New feature (non-breaking change which adds functionality)

Tests

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@calad0i calad0i marked this pull request as draft July 21, 2025 17:58
@calad0i calad0i added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels Jul 23, 2025
@calad0i calad0i marked this pull request as ready for review July 25, 2025 15:18
@calad0i calad0i mentioned this pull request Aug 7, 2025
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@calad0i calad0i added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels Aug 18, 2025
@calad0i calad0i mentioned this pull request Aug 19, 2025
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output_shape = get_output_shape(layer)
k, w, f = result_t.signed, result_t.width, result_t.fractional
i = w - k - f
k = np.full(output_shape, k, dtype=np.int8)
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Should this be int16 now following #1375?

@calad0i calad0i added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels Sep 6, 2025
@JanFSchulte
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For the quantized_bits quantizer, this seems to result in a duplication of the inputs, for example:

void myproject(
    result_t quantizer_input[1],
    result_t quantizer_input[1]
) { ...

This doesn't compile due to redefinition of the variable. Haven't dug deep enough to actually find the cause of the problem.

@calad0i
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calad0i commented Sep 8, 2025

For the quantized_bits quantizer, this seems to result in a duplication of the inputs, for example:

void myproject(
    result_t quantizer_input[1],
    result_t quantizer_input[1]
) { ...

This doesn't compile due to redefinition of the variable. Haven't dug deep enough to actually find the cause of the problem.

This test converts an orphaned quantizer. The fuse quantizer flow merged it with the input layer by overridding the precision, and there is 0 layer left in the model and hence inp=out, the duplicated name.

Made fuse only happen is bit-exact is enabled, though in this case I think removing this test would also make sense.

@JanFSchulte JanFSchulte added github_actions Pull requests that update GitHub Actions code please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch github_actions Pull requests that update GitHub Actions code labels Sep 8, 2025
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Now I see that in the hgq_layer tests all test_syn_players tests with io_steam fail with

NotImplementedError: Heterogenous quantization for activations is only supported with IOType=io_parallel

Can you have a look at that as well?

@calad0i
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calad0i commented Sep 8, 2025

Trigger cond was reversed

@calad0i calad0i added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels Sep 8, 2025
@JanFSchulte
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Last remaining test failure is already fixed by #1377. So this is good to go now.

@JanFSchulte JanFSchulte merged commit 71123c8 into fastmachinelearning:main Sep 8, 2025
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2 participants