[Relax][Frontend][TFLite] Support quantized TFLite import via QDQ decomposition

[Aharrypotter]

Summary

This PR adds initial quantized TFLite import support to the Relax frontend by
preserving tensor quantization metadata and replacing placeholder _qnn.op.*
frontend calls with an explicit QDQ decomposition:

dequantize -> float Relax op -> quantize

Before this PR, the Relax TFLite frontend raised NotImplementedError as soon
as quantization metadata was seen during tensor parsing. This made quantized
TFLite models unreachable. This PR keeps scale, zero_point, and
QuantizedDimension() in TensorWrapper.qnn_params, then uses the existing
R.quantize / R.dequantize operators to lower supported quantized paths.

Closes #19534.

Design

Relax already has R.quantize and R.dequantize with C++ registration, Python
APIs, legalization, and tests. Instead of introducing new fused Relax QNN ops
for this first import PR, the frontend now decomposes quantized TFLite operators
through QDQ around ordinary Relax float operators.

This keeps the change scoped to the Python TFLite frontend and existing Relax
QDQ operators, while establishing a working import path first. Fused int8 Relax
QNN operators can still be considered later if backend kernel selection requires
them.

Updated Converters

Converter	Replacement
get_tensors	Preserve scale, zero_point, and QuantizedDimension()
quantize / dequantize helpers	Use R.quantize / R.dequantize with axis
convert_quantize	float -> Q and quantized requantize as DQ -> Q
convert_dequantize	Use R.dequantize
convert_relu, convert_relu6, convert_relu_n1_to_1	DQ -> activation -> Q
_convert_elemwise	Quantized binary ops use DQ -> op -> fused activation -> Q; comparisons use DQ -> compare
convert_reshape	uint8 different-qparams path uses DQ -> reshape -> Q
_convert_reduce	Quantized reduce uses DQ -> reduce -> Q
convert_conv	Quantized Conv2D uses DQ input + DQ weight -> conv2d -> Q
convert_fully_connected	Quantized FC uses DQ input + DQ weight -> matmul -> Q
convert_concatenation	Quantized concat uses DQ each -> concat -> Q
convert_transpose_conv	Quantized transpose conv uses DQ input + DQ weight -> conv2d_transpose -> Q
convert_detection_postprocess	Inline _qnn.op.dequantize calls replaced with self.dequantize

All _qnn.op.* references are removed, and the stale # ruff: noqa: F821
suppression is no longer needed.

Axis Remapping

The most correctness-sensitive part of this PR is axis remapping for per-channel
weight dequantization after the frontend rewrites TFLite layouts into Relax
layouts.

Op	TFLite layout	Relax layout	Axis remap
Conv2D	[OC, KH, KW, IC]	[KH, KW, IC, OC] (HWIO)	0 -> 3
FullyConnected	[OC, IC]	[IC, OC]	0 -> 1
TransposeConv	[OC, KH, KW, IC] (OHWI)	[IC, OC, KH, KW] (IOHW)	0 -> 1
DepthwiseConv	[1, KH, KW, C*M]	[KH, KW, C, M] (HWOI)	per-channel unsupported

For Conv2D, FC, and TransposeConv, non-zero weight QuantizedDimension() values
are rejected with OpAttributeInvalid, because the supported quantized TFLite
weight layout uses output-channel axis 0.

Per-channel depthwise convolution is guarded with OpNotImplemented. The
TFLite depthwise reshape changes the channel-axis semantics in a way that this
initial QDQ lowering does not represent directly.

Bias Handling

TFLite INT32/INT64 bias tensors may not store explicit quantization metadata.
For quantized Conv2D, FullyConnected, and TransposeConv, the frontend follows
the implicit TFLite convention and dequantizes integer bias using:

bias_scale = input_scale * weight_scale
bias_zero_point = 0
axis = 0

This supports both per-tensor and per-channel weight scales. The per-channel
case is covered by a structural regression test that expects vector bias scale.

Fused Activation Handling

Most quantized convolution-like paths preserve the existing quantized-domain
fused activation behavior:

float op -> Q -> quantized-domain clip

The elemwise QDQ path applies fused activation before the final quantize:

DQ -> float binary op -> float fused activation -> Q

Both paths are intentional and covered by regression tests:

• quantized concat fused RELU checks the quantized-domain clip path
• quantized add fused RELU6 checks the float-domain activation-before-Q path

This PR also fixes a latent R.clip call-site bug in the quantized fused
RELU helper by using max= rather than the unsupported a_max= keyword.

Safety Checks

• Quantized elemwise non-comparison outputs must have output qparams. Missing
  output quantization metadata now raises OpAttributeInvalid instead of
  silently returning a float result.
• Per-channel quantization rejects non-zero per-axis zero points, following the
  TFLite quantization specification.
• Per-channel depthwise convolution is explicitly unsupported rather than
  importing with an incorrect axis interpretation.

Tests

The new tests build minimal TFLite flatbuffers directly and compare the imported
Relax IR with tvm.ir.assert_structural_equal. Unsupported-boundary tests use
pytest.raises.

Test	Coverage
test_tensor_quantization_parameters_are_parsed	per-tensor and per-axis metadata parsing
test_quantize_op_uses_relax_quantize	TFLite QUANTIZE float input
test_quantize_op_requantize_uses_dq_q	TFLite QUANTIZE as requantize
test_dequantize_op_uses_relax_dequantize	TFLite DEQUANTIZE
test_quantized_add_uses_qdq	quantized ADD with differing input qparams
test_quantized_add_fused_relu6_uses_float_clip_before_quantize	elemwise fused activation before Q
test_quantized_add_without_output_qparams_invalid	invalid missing output qparams guard
test_quantized_conv2d_per_tensor_uses_qdq	Conv2D per-tensor QDQ
test_quantized_conv2d_per_channel_weight_uses_remapped_axis	Conv2D per-channel weight axis 0 -> 3
test_quantized_conv2d_with_int32_bias_dequantizes_bias	Conv2D INT32 bias scale
test_quantized_conv2d_per_channel_weight_with_int32_bias_dequantizes_bias	Conv2D per-channel vector bias scale
test_quantized_concat_uses_qdq	concat QDQ path
test_quantized_concat_fused_relu_uses_quantized_clip	quantized-domain fused RELU clip
test_per_channel_depthwise_conv_unsupported	per-channel depthwise guard
test_uint8_reshape_requantize_uses_dq_reshape_q	uint8 reshape with different qparams
test_transpose_conv_with_int32_bias_dequantizes_bias	TransposeConv INT32 bias DQ
test_quantized_fully_connected_with_int32_bias_dequantizes_bias	FC INT32 bias DQ

Local validation:

python -m ruff format --check \
  python/tvm/relax/frontend/tflite/tflite_frontend.py \
  tests/python/relax/test_frontend_tflite.py

python -m ruff check \
  python/tvm/relax/frontend/tflite/tflite_frontend.py \
  tests/python/relax/test_frontend_tflite.py

python -m pytest tests/python/relax/test_frontend_tflite.py -q

Result:

433 passed

Limitations

• This PR prioritizes correct import and explicit Relax IR over fused int8
  kernel selection. The generated IR uses QDQ and float Relax operators.
• Per-channel depthwise convolution remains unsupported.
• The tests are structural IR tests. Numerical comparison against TFLite runtime
  outputs is left to follow-up work.

References

• Issue [Tracking Issue][TFLite] Support quantized operator import in Relax frontend #19534: Support quantized TFLite import in Relax frontend
• TFLite quantization spec: https://www.tensorflow.org/lite/performance/quantization_spec

[gemini-code-assist]

Code Review

This pull request implements support for quantized TFLite models in the Relax frontend by transitioning from specialized QNN operators to a dequantize-compute-quantize pattern using core Relax ops. Key changes include the addition of per-channel quantization support and updates to operators like convolution, fully connected, and activations. Feedback highlights potential runtime errors when handling per-channel quantized models due to the use of scalar-only constant extraction for scales; it is recommended to use relax.op.multiply to combine input and weight scales. Additionally, the reviewer suggests adding explicit validation for quantization axes in convolution and fully connected layers to ensure robustness against non-standard TFLite models.

[gemini-code-assist]

Using get_scalar_from_constant on weight_tensor.qnn_params["scale"] will cause an AssertionError for per-channel quantized models because the scale is a constant tensor, not a scalar. To support both per-tensor and per-channel quantization robustly, you should use relax.op.multiply on the scale expressions and pass the resulting expression directly to relax.op.dequantize.

Suggested change

	bias_scale_val = (
	get_scalar_from_constant(input_tensor.qnn_params["scale"])
	* get_scalar_from_constant(weight_tensor.qnn_params["scale"])
	)
	bias_expr = relax.op.dequantize(
	bias_expr,
	scale=relax.const(bias_scale_val, "float32"),
	zero_point=relax.const(0, "int32"),
	axis=0,
	)
	elif input_tensor.qnn_params and bias_tensor_type in (
	TensorType.INT32,
	TensorType.INT64,
	):
	bias_scale = relax.op.multiply(
	input_tensor.qnn_params["scale"],
	weight_tensor.qnn_params["scale"],
	)
	bias_expr = relax.op.dequantize(
	bias_expr,
	scale=bias_scale,
	zero_point=relax.const(0, "int32"),
	axis=0,
	)

[Aharrypotter]

Done — replaced get_scalar_from_constant with relax.op.multiply so the bias scale works for both per-tensor and per-channel weight quantization. The multiply result is passed directly to relax.op.dequantize as a Relax expression.

[gemini-code-assist]

This get_scalar_from_constant call will crash on per-channel quantized TransposeConv models. Use relax.op.multiply to combine the input and weight scales into a single expression for the bias dequantization.

                bias_expr = relax.op.dequantize(
                    bias_expr,
                    scale=relax.op.multiply(
                        input_tensor.qnn_params["scale"],
                        weights_tensor.qnn_params["scale"],
                    ),
                    zero_point=relax.const(0, "int32"),
                    axis=0,
                )

[Aharrypotter]

Done — replaced get_scalar_from_constant with relax.op.multiply so the bias scale works for both per-tensor and per-channel weight quantization. The multiply result is passed directly to relax.op.dequantize as a Relax expression.

[Aharrypotter]

cc @tlopex

[tlopex]

Please resolve the conflict

[Aharrypotter]

Rebased the branch and resolved the merge conflicts.

During the post-rebase check, I found a few related issues and fixed them in the same update:

• Avoid generated tflite FlatBuffer builder APIs in tests, since they are not available in CI.
• Make the quantized elemwise path follow the same DQ -> float op -> Q pattern used by the other quantized converters.
• Add regression tests for quantized elemwise ops, fused activation handling, and per-channel Conv2D bias / axis remapping.

These changes are closely related to the rebase and should help keep the PR passing.

[Aharrypotter]

Update PR description