[QNN] Requantize operator #3531

anijain2305 · 2019-07-10T22:05:26Z

Requantize converts one quantized tensor representation to another quantized
representation. The PR has following implementation features

Relevant Issue - #2351

Requantize operator defined in qnn namespace - relay.op.qnn.requantize
Lowering of the requantize to exisiting Relay operators
Integer fixed point implementation of requantize
- Two rounding modes - FE_UPWARDS (round towards infinity) and
  FE_AWAY_FROM_ZERO (std::round behavior)
Floating point implementation as well, that can act as reference or can be
used for devices when FP32 computation is not expensive.
Unit test cases

Credit to TFLite and GemmLowp to provide reference implementations.

Thanks for contributing to TVM! Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from Reviewers.

tqchen · 2019-07-10T22:10:23Z

Let us distinguish the qnn from the quantize namespace, given that the primary lowering phase supports in src/relay/qnn, we can also discuss the python namespace, perhaps relay.qnn. which is different from the current quantize namespace(automatic quantization in relay from float models)

anijain2305 · 2019-07-10T22:36:03Z

@tqchen Thanks for the quick comment :)

Can you please elaborate a little bit? Do you mean to change this file - https://github.com/dmlc/tvm/pull/3531/files#diff-dcae58edc1986609a54e3a202ee49b3b

anijain2305 · 2019-07-10T22:45:57Z

python/tvm/relay/quantize/rewrite.py

@@ -0,0 +1,37 @@
+# Licensed to the Apache Software Foundation (ASF) under one


@tqchen Does it make sense to move this to newly created directory - python/tvm/relay/op/qnn?

This will ensure the quantize python namespace is not polluted.

anijain2305 · 2019-07-10T22:47:21Z

src/relay/pass/quantize_rewrite.cc

@@ -0,0 +1,262 @@
+/*


@tqchen Should l call this file qnn_rewrite.cc to have a clear marking between quantize (Quantization in Relay) and qnn (creating new quantized ops) work?

anijain2305 · 2019-07-10T23:17:28Z

@FrozenGene Please have a look.

anijain2305 · 2019-07-10T23:56:56Z

src/relay/pass/quantize_rewrite.cc

+}
+
+RELAY_REGISTER_OP("qnn.requantize")
+.set_attr<FForwardRewrite>("FQuantizeForwardRewrite", RequantizeForwardRewrite);


@tqchen Does it make sense to replace FQuantizeForwardRewrite with FQnnForwardRewrite (for differentiating qnn and quantize)?

tqchen · 2019-07-11T00:27:51Z

Given that qnn is a dialect. We could consider giving it a special name place and folder to live. e.g.

src/relay/qnn
src/relay/op/qnn
tvm.relay.qnn

We can debate on whether it should be relay/qnn/op or relay/op/qnn cc @jroesch Perhaps let us open an RFC for dialect convention.

I have not yet taken a deep look into the PR, but here are a few high-level comments

Header files, for utilities etc, we want to move most of them into src/ folder instead of include
- The idea is that everything in the include is public API(which external project can depend on), everything else should be in src/
There are a few style issues, we mainly use CamelCase for our APIs(as per Google C style).

anijain2305 · 2019-07-11T00:36:14Z

I see. Makes sense. I don't have a strong preference for any of the namespaces that you suggested. My favorite is src/relay/qnn. In this case, we are only dealing with ops. But, I can believe that some other dialect might want to more than just ops. But, I am ok with any of the above options.
Please let me know. I can refactor to see how it looks like.

Thanks for the high-level comments. I will fix the utils and put them in the src/relay/qnn directory. Will look for CamelCase. (I was hoping that lint would do that for me, but I guess not).

anijain2305 · 2019-07-11T06:25:05Z

@tqchen I have updated the code. The C++ files are in src/relay/qnn directory. It has its own sub-directories like include, op, pass to serve different purposes. Let me know how it looks.

FrozenGene · 2019-07-11T06:28:14Z

python/tvm/relay/op/qnn/qnn.py

+from . import _make
+
+def requantize(input_data, input_zero_point, input_scale, output_zero_point,
+               output_scale, out_dtype="int32", use_int_compute=False,


The following relay attr's default value is true. Please unify the default value (We should set it be True).

Good catch. Thanks :)

FrozenGene · 2019-07-11T06:34:35Z

python/tvm/relay/op/qnn/qnn.py

+
+def requantize(input_data, input_zero_point, input_scale, output_zero_point,
+               output_scale, out_dtype="int32", use_int_compute=False,
+               rounding_mode="FE_UPWARD"):


TFLite requires "round_awar_from_zero". Could you explain why we set it "FE_UPWARD" be the default value? Because of MXNet's requirement?

It can be "round_away_from_zero". These two rounding modes provide two points in the performance accuracy tradeoff curve. FE_UPWARD has fewer operators compared to round_way_from_zero. But, I think you are right, we should set "round_away_from_zero" by default. If somebody wants better performance, they can set it to FE_UPWARD using a network-level config.

FrozenGene · 2019-07-11T06:48:00Z

src/relay/qnn/pass/quantize_rewrite.cc

+  // input_tensor, the result in int64 where the decimal point is sitting
+  // between bits 31 and 30 (from the right, rightmost bit is bit 0).
+  Expr scalar = MakeConstantScalar(up_idtype, fixed_point_multiplier);
+  auto multiplied_t = Multiply(tensor, scalar);


Where is overflow logic? If it is overflow, we return int32_max.

Will look into this. I remember seeing an overflow check. Thanks for pointing out.

@FrozenGene Thought little more about it. I think we don't need this check. Please let me know if this makes sense.

The relevant portion from TFLite is

bool overflow = a == b && a == std::numeric_limits<std::int32_t>::min();

Here a is input tensor and b is the fixed_point_multiplier. The fixed_point_multiplier is calculated using frexp.

Now, frexp gives negative fixed point multiplier only when the input floating point number is negative. In our case, this floating point number is input_scale/output_scale. I think the scales are always positive, so we should never have negative number. (Let me know if I am wrong)

src/relay/qnn/pass/quantize_rewrite.cc

zhiics

I made a pass on it. It is mainly style stuff.

zhiics · 2019-07-11T06:51:33Z

python/tvm/relay/op/qnn/__init__.py

+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=wildcard-import
+"""Neural network related operators."""


Quantized neural network?

zhiics · 2019-07-11T16:30:13Z

src/relay/pass/pattern_util.h

+}
+
+inline Expr Full(Expr fill_value,
+              Array<IndexExpr> shape,


zhiics · 2019-07-11T16:33:10Z

src/relay/qnn/include/attrs.h

+  }
+};
+
+


remove one blank line

zhiics · 2019-07-11T16:33:41Z

src/relay/qnn/include/util.h

+
+/*!
+ * \file tvm/relay/quantize_util.h
+ * \brief Utility methods needs for quantized ops that can be shared


zhiics · 2019-07-11T16:49:52Z

src/relay/qnn/op/requantize.cc

+RELAY_REGISTER_OP("qnn.requantize")
+.describe(R"code(Requantize operator.
+
+FIXME


zhiics · 2019-07-11T17:27:18Z

src/relay/qnn/pass/quantize_rewrite.cc

+
+  // 2) Subtract the input_zero_point
+  auto tensor = input_tensor;
+  tensor = Cast(tensor, up_idtype);


auto tensor = Cast(input_tensor, up_idtype); ?

zhiics · 2019-07-11T17:27:53Z

src/relay/qnn/pass/quantize_rewrite.cc

+    auto input_zp = MakeConstantScalar(up_idtype, param->input_zero_point);
+    tensor = Subtract(tensor, input_zp);
+  }
+


Remove blank lines

zhiics · 2019-07-11T17:28:54Z

src/relay/qnn/pass/quantize_rewrite.cc

+  Expr scalar = MakeConstantScalar(up_idtype, fixed_point_multiplier);
+  auto multiplied_t = Multiply(tensor, scalar);
+
+


remove one blank line

zhiics · 2019-07-11T17:30:39Z

src/relay/qnn/pass/quantize_rewrite.cc

+
+  // 4) Find the rounding scalar. This depends on where the final decimal point
+  // sits. As we will be right shifting the multiplied_t, we need to first
+  // calculate the totol_right_shift.


total_right_shift

zhiics · 2019-07-11T17:49:08Z

tests/python/unittest/test_quantized_ops.py

+
+
+
+        if __name__ == "__main__":


move xx_test under main below directly? run_test looks not necessary

how about separate them to individual test_ functions instead of packing together to one giant test_requantize?

FrozenGene · 2019-07-12T09:19:59Z

tests/python/unittest/test_quantized_ops.py

+        return func
+
+
+    def run_tests():


Add uint8 test. Like TFLite, whose out dtype is uint8. Let us cover it.

Good point. Will add it by tonight.

tqchen · 2019-07-12T17:09:16Z

src/relay/qnn/util.h

+namespace tvm {
+namespace relay {
+
+inline bool IsInt8(const DataType& dtype) {


This is certainly a too small function to introduce another level of indirection. Directly remove it and keep things in the original condition

tqchen · 2019-07-12T17:09:31Z

src/relay/qnn/util.h

+ */
+
+/*!
+ * \file tvm/relay/quantize_util.h


wrong file name

tqchen · 2019-07-12T17:10:06Z

src/relay/qnn/util.h

+      || IsInt16(dtype) || IsUint16(dtype);
+}
+
+enum class QuantizeOpType : uint8_t {


unless there is a need(data compactness), you don't want to use uint8_t as storage type

tqchen · 2019-07-12T17:10:12Z

src/relay/qnn/util.h

+  return dtype == Float(32);
+}
+
+inline bool IsQuantizedType(const DataType& dtype) {


IsQNNDataType

tqchen · 2019-07-12T17:11:35Z

src/relay/qnn/pass/quantize_rewrite.cc

+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file


rename to qnn_lower.cc

tqchen · 2019-07-12T17:12:28Z

python/tvm/relay/qnn/op/qnn.py

+from __future__ import absolute_import as _abs
+from . import _make
+
+def requantize(input_data, input_zero_point, input_scale, output_zero_point,


please debate on API naming, especially refer to possible existing API names and choices of options

tqchen · 2019-07-12T17:14:06Z

I made a few quick reviews. @FrozenGene @zhiics @antinucleon @hlu1 please help check again if you have time.

Let us specifically spend some time to discuss the API, in particular, what are the choices of parameter names, order and the options, do they make sense or not. Is there existing APIs that we can be consistent with etc.

yzhliu · 2019-07-12T17:29:42Z

include/tvm/relay/qnn/attrs.h

+        .describe("Defines the rounding direction when the value is midway between"
+                  "two representable values. There are two supported modes - FE_UPWARD"
+                  "or FE_AWAY_FROM_ZERO. More context can be found at"
+                  "https://www.gnu.org/software/libc/manual/html_node/Rounding.html");


suggest to explain the meaning of the two modes here as well, so to make it self-explained.

Thanks, added the description.

yzhliu · 2019-07-12T17:32:38Z

python/tvm/relay/qnn/op/qnn.py

+
+    Parameters
+    ----------
+    quantized_data : tvm.relay.Expr


doc does not match the arguments in the function

yzhliu · 2019-07-12T17:48:32Z

src/relay/qnn/util.h

+  } else if (IsUint32(dtype)) {
+    return std::numeric_limits<uint32_t>::min();
+  }
+  LOG(FATAL) << "Type not supported\n";


also << dtype to make debug more easier. also I think we can remove "\n"

yzhliu · 2019-07-12T17:51:11Z

src/relay/qnn/pass/quantize_rewrite.cc

+RELAY_REGISTER_OP("qnn.requantize")
+.set_attr<FForwardRewrite>("FQuantizeForwardRewrite", RequantizeForwardRewrite);
+
+TVM_REGISTER_API("relay._qnn.rewrite")


should this be ported to pass manager? @zhiics

Talked to @anijain2305 offline, it sounds this pass is more or less a dialect that is only performed in the frontend parsers. I think we probably don't want to bring it into the pass manager because 1) it is quite frontend specific, 2) it only does Expr -> Expr transformation.

We could probably use better naming instead of rewrite here and ir_pass in Python. Since we have pass under the dialect fold, should we have transform as well?

@tqchen thoughts?

Yes, I think we need to use transform

yzhliu · 2019-07-12T17:52:55Z

src/relay/qnn/pass/quantize_rewrite.cc

+    const Array<Expr>& new_args, const NodeRef& ctx) {
+  CHECK_EQ(new_args.size(), 1);
+  Expr quantized_data = new_args[0];
+  const auto* param = ref_call->attrs.as<RequantizeAttrs>();


if param == nullptr ?

yzhliu · 2019-07-12T17:53:53Z

src/relay/qnn/util.h

+      return IsFloat32(in_dtype) || IsQuantizedType(in_dtype);
+    case QuantizeOpType ::Dequantize:
+      return IsQuantizedType(in_dtype);
+    case QuantizeOpType ::Requantize:


remove space before ::

yzhliu · 2019-07-12T17:57:20Z

tests/python/unittest/test_quantized_ops.py

+
+
+
+        if __name__ == "__main__":


how about separate them to individual test_ functions instead of packing together to one giant test_requantize?

shoubhik · 2019-07-12T21:48:18Z

I made a few quick reviews. @FrozenGene @zhiics @antinucleon @hlu1 please help check again if you have time.

Let us specifically spend some time to discuss the API, in particular, what are the choices of parameter names, order and the options, do they make sense or not. Is there existing APIs that we can be consistent with etc.

Can you also look at #3512 from API name perspective as well.

anijain2305 · 2019-07-13T23:18:32Z

Let me write down my initial pass over the API design. Please help me refine it further.

Order of parameters

// All input relay exprs
input1_relay_expr,
input2_relay_expr,
...
...

// Properties for the input relay expr alphabetically (for example zero_point)
input1_prop1,
input1_prop2,
...
input2_prop1,
input2_prop2,

// Properties for the output relay expr alphabetically (for example zero_point)
output1_prop1,
output1_prop2,

// Finally, the properties of the ops alphabetically (like use_bias, use_int_compute)
op_prop1
op_prop2

Parameter names

I could not find a specific pattern in the names. However, I think these are some common rules

parameter name should be concise
If there are multiple choices for the same param, most widely used one can be chosen to retain readability.
for bool params, a good name can describe the action (E.g count_include_pad, clip). There are few examples in the codebase that do not follow this rule.

Current proposal

Adhering to above rules, the API looks like this

def requantize(data,
               # Input expr properties
               input_scale,
               input_zero_point,

               # Output expr properties
               output_scale,
               output_zero_point,

               # Op properties
               out_dtype,
               rounding, # _mode seemed unnecessary
               use_int_domain, # compute was somewhat ambiguous               
               ):

anijain2305 · 2019-07-15T17:52:29Z

Thanks for all the comments. I fixed them. Please review once again.
Currently changed the API as per above proposal.

tqchen · 2019-07-16T18:00:51Z

Can we also list the related APIs from existing frameworks e.g. tflite so we can get a good reference. Right now I can understand most of the parameters, except use_int_domain was a bit unclear to me

anijain2305 · 2019-07-16T18:29:55Z

For TF, the requantize operator looks like this

Arguments:

scope: A Scope object
input_min: The float value that the minimum quantized input value represents.
input_max: The float value that the maximum quantized input value represents.
requested_output_min: The float value that the minimum quantized output value represents.
requested_output_max: The float value that the maximum quantized output value represents.
out_type: The type of the output. Should be a lower bit depth than Tinput.

I think all the above arguments are represented in current API proposal (min/max are represented as scale/zero_point).

use_int_domain gives an ability to run the computation in FP32 domain. The integer domain has more instructions due to correct rounding, compared to FP32 computation. So, there can be a tradeoff for different HW targets.

However, if you think we will never use FP32 computation for requantize, that is also a reasonable answer.

FrozenGene · 2019-07-17T02:36:12Z

Maybe we don't need use_int_domain. I can not imagine one HW we will set this be false.

anijain2305 · 2019-07-17T06:06:03Z

Thanks @FrozenGene and @tqchen. Spent some more time thinking about it. Realized that it might not be necessary. Removed use_int_domain.

Please review again.

anijain2305 · 2019-07-18T21:03:55Z

@tqchen Please let me know if you got a chance to look at the changes.

By the way, phisiart was added for review automatically when I was rebasing. Maybe, I made a mistake in rebasing. I don't know how to remove the reviewer.

…s uint8.

…int32.

anijain2305 · 2019-08-08T15:50:55Z

@tqchen Can we try to get this merged if everything looks good? Will open up discussion/review for many other PRs.

tqchen · 2019-08-08T18:42:57Z

Thanks @anijain2305 @u99127 @FrozenGene , @yzhliu @jackwish @zhiics @shoubhik

* [Relay] [Quantization] WIP - Common files for the qauntization work. * [Relay] [Quantization] WIP - Prototyping requantize op. * Requantize operator implementation. Requantize converts one quantized tensor representation to another quantized representation. The PR has following implementation features - Requantize operator defined in qnn namespace - relay.qnn.requantize - Lowering of the requantize to exisiting Relay operators - Integer fixed point implementation of requantize - Two rounding modes - FE_UPWARDS (round towards infinity) and FE_AWAY_FROM_ZERO (std::round behavior) - Floating point implementation as well, that can act as reference or can be used for devices when FP32 computation is not used. - Unit test cases Relevant Issue - apache#2351 Credit to TFLite and GemmLowp to provide reference implementations. * Typo and lint fixes. * Doc fix. * Uncommenting the lint script (fixing mistake). * Modifying the unit tests. * Moving C++ files into src/relay/qnn * Moving python files to python/tvm/relay/qnn. Some minor fixes. * Moving the attrs.h inside the include directory. * Pushing files that I forgot earlier. Changing util location. * Incorporating comments. API change. Lint fixes. * Modifying the GetFixedPointMultiplierShift API as per comments. * Forgot the dialect change. * Changing rewrite to qnn_lower. * Renaming Quantize to Qnn for clarity. * Remove use_int_domain. * Incorportaing review comments. * Adding API doc for QNN dialect. * Move the qnn_lower pass to transform namespace. * Moving from expr to module. Adding namespace in C++. * Minor sentence rewrites. Added qnn namespace. * Added the API doc. * Chanding default out_dtype to int8. Adding a test with in/out_dtype as uint8. * Style fixes. Better error messages. * Adding documentation. * More documentation fixes. * Adding out dtype check for requantize. * Adding corner case for FP32 to fixed point conversion. * Adding extra line. * Documentation fix. * Adding static inline. * Incorporating jackwish comment. Removed idtype from requantize lowering. * Removing Quantize/Dequantize code. Restricting Requantize to (u)int8/int32. * Style fixes. * Fix the docs. * Move to Legalize API.

anijain2305 commented Jul 10, 2019

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FrozenGene reviewed Jul 11, 2019

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tqchen added the status: need review label Jul 11, 2019

tqchen changed the title ~~Requantize operator~~ [QNN] Requantize operator Jul 11, 2019

FrozenGene reviewed Jul 12, 2019

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tqchen mentioned this pull request Jul 12, 2019

[RFC][Quantization] Designing and lowering of quantized ops #3512

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anijain2305 requested a review from phisiart as a code owner July 15, 2019 15:46

anijain2305 force-pushed the requantize branch from 38573ef to 4a4beec Compare July 15, 2019 16:00

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anijain2305 mentioned this pull request Jul 18, 2019

[QNN] Conv2D operator #3580

Merged

anijain2305 added 18 commits August 8, 2019 00:46

Move the qnn_lower pass to transform namespace.

435ca27

Moving from expr to module. Adding namespace in C++.

e4f6a4e

Minor sentence rewrites. Added qnn namespace.

10a20d3

Added the API doc.

927825d

Chanding default out_dtype to int8. Adding a test with in/out_dtype a…

48f5a52

…s uint8.

Style fixes. Better error messages.

1422f6d

Adding documentation.

66a4d76

More documentation fixes.

99483c2

Adding out dtype check for requantize.

f8439e6

Adding corner case for FP32 to fixed point conversion.

e756843

Adding extra line.

5d7938f

Documentation fix.

10ce99d

Adding static inline.

f2e09d1

Incorporating jackwish comment. Removed idtype from requantize lowering.

65c0b46

Removing Quantize/Dequantize code. Restricting Requantize to (u)int8/…

8d2c3ad

…int32.

Style fixes.

2d15b54

Fix the docs.

ff17a91

Move to Legalize API.

c46b56c

anijain2305 force-pushed the requantize branch from a4eecd5 to c46b56c Compare August 8, 2019 00:47

tqchen approved these changes Aug 8, 2019

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tqchen merged commit a78adbd into apache:master Aug 8, 2019

tqchen added status: accepted and removed status: need review labels Aug 8, 2019

shoubhik mentioned this pull request Aug 9, 2019

QNN quantize and dequantize operators. #3745

Merged

tqchen mentioned this pull request Nov 8, 2019

[RELEASE][DRAFT] TVM v0.6 Release candidate #4259

Closed

anijain2305 deleted the requantize branch November 13, 2019 00:30

		@@ -0,0 +1,37 @@
		# Licensed to the Apache Software Foundation (ASF) under one

		Expr scalar = MakeConstantScalar(up_idtype, fixed_point_multiplier);
		auto multiplied_t = Multiply(tensor, scalar);

[QNN] Requantize operator #3531

[QNN] Requantize operator #3531

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shoubhik commented Jul 12, 2019

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Order of parameters

Parameter names

Current proposal

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