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teflon: Reject per-axis quantization

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Until a workaround for missing hardware support is implemented, stop
pretending to support convolution operations on tensors with per-axis
quantization.

Reviewed-by: Tomeu Vizoso <tomeu@tomeuvizoso.net>
Signed-off-by: Philipp Zabel <p.zabel@pengutronix.de>
Part-of: <https://gitlab.freedesktop.org/mesa/mesa/-/merge_requests/32387>
This commit is contained in:
Philipp Zabel 2024-11-26 16:47:47 +01:00 • committed by Marge Bot
parent 0501a3b5c1
commit a9f0624d6b
1 changed files with 35 additions and 4 deletions
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39
src/gallium/frontends/teflon/tfl_device.c
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@ -390,6 +390,21 @@ partition_invoke(TfLiteContext *tf_context, TfLiteNode *node)
return kTfLiteOk;
}
static bool
tensor_quantization_supported(TfLiteTensor *tensor)
{
if (tensor->quantization.type == kTfLiteAffineQuantization) {
TfLiteAffineQuantization *affine = (TfLiteAffineQuantization *)tensor->quantization.params;
/*
* Per-axis quantization not supported, for details see:
* https://ai.google.dev/edge/litert/models/quantization_spec#per-axis_vs_per-tensor
*/
return affine->scale->size == 1 && affine->zero_point->size == 1;
}
return false;
}
static TfLiteStatus
PrepareDelegate(TfLiteContext *context, TfLiteDelegate *delegate)
{
@ -410,10 +425,18 @@ PrepareDelegate(TfLiteContext *context, TfLiteDelegate *delegate)
switch(registration->builtin_code) {
case kTfLiteBuiltinConv2d: {
TfLiteTensor *input_tensor = &context->tensors[node->inputs->data[0]];
TfLiteTensor *weight_tensor = &context->tensors[node->inputs->data[1]];
TfLiteTensor *bias_tensor = &context->tensors[node->inputs->data[2]];
TfLiteTensor *output_tensor = &context->tensors[node->outputs->data[0]];
TfLiteConvParams* params = (TfLiteConvParams*)node->builtin_data;
// Dilation not yet implemented
if ((params->activation == kTfLiteActNone ||
// Dilation and per-axis quantization not yet implemented
if (tensor_quantization_supported(input_tensor) &&
tensor_quantization_supported(weight_tensor) &&
tensor_quantization_supported(bias_tensor) &&
tensor_quantization_supported(output_tensor) &&
(params->activation == kTfLiteActNone ||
params->activation == kTfLiteActRelu) &&
(registration->version < 2 ||
(params->dilation_width_factor == 1 &&
@ -423,10 +446,18 @@ PrepareDelegate(TfLiteContext *context, TfLiteDelegate *delegate)
break;
}
case kTfLiteBuiltinDepthwiseConv2d: {
TfLiteTensor *input_tensor = &context->tensors[node->inputs->data[0]];
TfLiteTensor *weight_tensor = &context->tensors[node->inputs->data[1]];
TfLiteTensor *bias_tensor = &context->tensors[node->inputs->data[2]];
TfLiteTensor *output_tensor = &context->tensors[node->outputs->data[0]];
TfLiteDepthwiseConvParams* params = (TfLiteDepthwiseConvParams*)node->builtin_data;
// Dilation not yet implemented
if ((params->activation == kTfLiteActNone ||
// Dilation and per-axis quantization not yet implemented
if (tensor_quantization_supported(input_tensor) &&
tensor_quantization_supported(weight_tensor) &&
tensor_quantization_supported(bias_tensor) &&
tensor_quantization_supported(output_tensor) &&
(params->activation == kTfLiteActNone ||
params->activation == kTfLiteActRelu) &&
(registration->version < 2 ||
(params->dilation_width_factor == 1 &&