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[TOPI][Hexagon] Implement quantized elementwise for hexagon (#12606)
* [TOPI][Hexagon] Add test and schedule for uint8 resize2d * Fix correctness issue * Reformat * [TOPI][Hexagon] Implement quantized elementwise * Reformat * Address review comments * Reformat * Revert * Address review comments
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# 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 | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
# pylint: disable=invalid-name | ||
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"""Compute and schedule for quantized add, multiply, subtract op | ||
Please note the following assumptions made by the implementation: | ||
1) The inputs will be multiple of crouton layout except for the axis that needs broadcasting.""" | ||
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from tvm import te | ||
from tvm import tir | ||
from ..utils import get_layout_transform_fn, get_fixed_point_value | ||
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def broadcast_axis(tensor_A, tensor_B): | ||
"""Find out the indices that will have broadcasting""" | ||
A_broadcast = [] | ||
B_broadcast = [] | ||
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for i in range(len(tensor_A.shape)): | ||
if tensor_A.shape[i] == tensor_B.shape[i]: | ||
A_broadcast.append(1) | ||
B_broadcast.append(1) | ||
elif tensor_A.shape[i] == 1: | ||
A_broadcast.append(0) | ||
B_broadcast.append(1) | ||
elif tensor_B.shape[i] == 1: | ||
A_broadcast.append(1) | ||
B_broadcast.append(0) | ||
return A_broadcast, B_broadcast | ||
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def saturate(x: te.Tensor, dtype: str): | ||
"""Saturate value for the specified data type""" | ||
return te.max(te.min_value(dtype), te.min(x, te.max_value(dtype))) | ||
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def get_int_scale( | ||
scale_A: float, | ||
scale_B: float, | ||
scale_M: float, | ||
zero_point_A: int, | ||
zero_point_B: int, | ||
zero_point_M: int, | ||
op: str, | ||
): | ||
""" | ||
Get fixed-point number and exp_scale_factor from topi.hexagon.utils.get_fixed_point_value. | ||
Also, depending on the op, this function uses exp_scale_factor(log2 of the scale factor) | ||
to adjust the output's zero_point. | ||
""" | ||
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C_recip = 1 / scale_M | ||
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if op == "qmul": | ||
scale = scale_A * scale_B * C_recip | ||
scale_fixed_point, rsh = get_fixed_point_value(scale, "int16") | ||
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# We need to adjust output's zero point value since the compute for the op is multiplied | ||
# by a scaling factor. | ||
# The scaling factor is 2^x where x is the exp_scale_factor which is assigned to rsh here. | ||
# Since zero_point_M is multipled by 2^rsh while converting floating-point scale value | ||
# into fixed-point number, we left shift it by rsh in our compute to reflect that. | ||
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corr = zero_point_M << rsh | ||
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return scale_fixed_point, rsh, corr | ||
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a_scale_f = scale_A * C_recip | ||
b_scale_f = scale_B * C_recip | ||
scale_fixed_point_a, rsh_a = get_fixed_point_value(a_scale_f, "int16") | ||
scale_fixed_point_b, rsh_b = get_fixed_point_value(b_scale_f, "int16") | ||
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# Here we have two exp_scale_factors rsh_a and rsh_b. | ||
# To avoid complexity, we want to use a common exp_scale_factor and | ||
# we want to use the lowest of the two. | ||
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# Since, either of scale_fixed_point_a or scale_fixed_point_b has already been multiplied | ||
# by 2^max(rsh_a, rsh_b) in topi.hexagon.utils.get_fixed_point_value, | ||
# we want to undo that by right shifting that scale_fixed_point value | ||
# by the difference of rsh_a and rsh_b. | ||
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# This results into having a common exp_scale_factor for both scale_fixed_point_a | ||
# and scale_fixed_point_b. | ||
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# We also set rsh here which is used to adjust the zero_point_M and compute the corr value, | ||
# computation of which comes from the original equation of the op's compute. | ||
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if rsh_a > rsh_b: | ||
scale_fixed_point_a = scale_fixed_point_a >> (rsh_a - rsh_b) | ||
rsh = rsh_b | ||
else: | ||
scale_fixed_point_b = scale_fixed_point_b >> (rsh_b - rsh_a) | ||
rsh = rsh_a | ||
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if op == "qadd": | ||
corr = (zero_point_M << rsh) - ( | ||
zero_point_A * scale_fixed_point_a + zero_point_B * scale_fixed_point_b | ||
) | ||
else: | ||
corr = (zero_point_M << rsh) - ( | ||
zero_point_A * scale_fixed_point_a - zero_point_B * scale_fixed_point_b | ||
) | ||
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return scale_fixed_point_a, scale_fixed_point_b, rsh, corr | ||
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def qadd_broadcast_compute( | ||
tensor_A: te.Tensor, | ||
tensor_B: te.Tensor, | ||
output_shape: list, | ||
zero_point_A: int, | ||
scale_A: float, | ||
zero_point_B: int, | ||
scale_B: float, | ||
zero_point_M: int, | ||
scale_M: float, | ||
dtype: str, | ||
): | ||
"""Compute quantized add with broadcasting""" | ||
A_broadcast, B_broadcast = broadcast_axis(tensor_A, tensor_B) | ||
n_a, h_a, w_a, c_a = A_broadcast | ||
n_b, h_b, w_b, c_b = B_broadcast | ||
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scale_a, scale_b, rsh, corr = get_int_scale( | ||
scale_A, scale_B, scale_M, zero_point_A, zero_point_B, zero_point_M, "qadd" | ||
) | ||
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return te.compute( | ||
output_shape, | ||
lambda n, h, w, c: saturate( | ||
( | ||
( | ||
(tensor_A[n * n_a, h * h_a, w * w_a, c * c_a] * scale_a) | ||
+ (tensor_B[n * n_b, h * h_b, w * w_b, c * c_b] * scale_b) | ||
+ corr | ||
) | ||
>> rsh | ||
), | ||
dtype, | ||
).astype(dtype), | ||
) | ||
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def qsubtract_broadcast_compute( | ||
tensor_A: te.Tensor, | ||
tensor_B: te.Tensor, | ||
output_shape: list, | ||
zero_point_A: int, | ||
scale_A: float, | ||
zero_point_B: int, | ||
scale_B: float, | ||
zero_point_M: int, | ||
scale_M: float, | ||
dtype: str, | ||
): | ||
"""Compute quantized subtract with broadcasting""" | ||
A_broadcast, B_broadcast = broadcast_axis(tensor_A, tensor_B) | ||
n_a, h_a, w_a, c_a = A_broadcast | ||
n_b, h_b, w_b, c_b = B_broadcast | ||
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scale_a, scale_b, rsh, corr = get_int_scale( | ||
scale_A, scale_B, scale_M, zero_point_A, zero_point_B, zero_point_M, "qsub" | ||
) | ||
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return te.compute( | ||
output_shape, | ||
lambda n, h, w, c: saturate( | ||
( | ||
( | ||
(tensor_A[n * n_a, h * h_a, w * w_a, c * c_a] * scale_a) | ||
- (tensor_B[n * n_b, h * h_b, w * w_b, c * c_b] * scale_b) | ||
+ corr | ||
) | ||
>> rsh | ||
), | ||
dtype, | ||
).astype(dtype), | ||
) | ||
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def qmultiply_broadcast_compute( | ||
tensor_A: te.Tensor, | ||
tensor_B: te.Tensor, | ||
output_shape: list, | ||
zero_point_A: int, | ||
scale_A: float, | ||
zero_point_B: int, | ||
scale_B: float, | ||
zero_point_M: int, | ||
scale_M: float, | ||
dtype: str, | ||
): | ||
"""Compute quantized multiply with broadcasting""" | ||
A_broadcast, B_broadcast = broadcast_axis(tensor_A, tensor_B) | ||
n_a, h_a, w_a, c_a = A_broadcast | ||
n_b, h_b, w_b, c_b = B_broadcast | ||
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scale_int, rsh, corr = get_int_scale( | ||
scale_A, scale_B, scale_M, zero_point_A, zero_point_B, zero_point_M, "qmul" | ||
) | ||
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return te.compute( | ||
output_shape, | ||
lambda n, h, w, c: saturate( | ||
( | ||
( | ||
scale_int | ||
* (tensor_A[n * n_a, h * h_a, w * w_a, c * c_a] - zero_point_A) | ||
* (tensor_B[n * n_b, h * h_b, w * w_b, c * c_b] - zero_point_B) | ||
+ corr | ||
) | ||
>> rsh | ||
), | ||
dtype, | ||
).astype(dtype), | ||
) | ||
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def tir_schedule_quant( | ||
out_M: te.Tensor, | ||
tensor_A: te.Tensor, | ||
tensor_B: te.Tensor, | ||
output_layout: str, | ||
tensor_A_layout: str, | ||
tensor_B_layout: str, | ||
): | ||
"""Schedule for output layout nhwc-8h8w32c-2d""" | ||
func = te.create_prim_func([tensor_A, tensor_B, out_M]) | ||
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s = tir.Schedule(func) | ||
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block = s.get_block("compute") | ||
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if tensor_A_layout == "nhwc-8h8w32c-2d": | ||
tensor_A_transformed_layout = get_layout_transform_fn(tensor_A_layout) | ||
s.transform_layout(block, buffer=tensor_A.name, index_map=tensor_A_transformed_layout) | ||
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if tensor_B_layout == "nhwc-8h8w32c-2d": | ||
tensor_B_transformed_layout = get_layout_transform_fn(tensor_B_layout) | ||
s.transform_layout(block, buffer=tensor_B.name, index_map=tensor_B_transformed_layout) | ||
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output_transformed_layout = get_layout_transform_fn(output_layout) | ||
s.transform_layout(block, buffer=out_M.name, index_map=output_transformed_layout) | ||
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n, h, w, c = s.get_loops(block) | ||
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h_o, h_i = s.split(h, [None, 8]) | ||
w_o, w_i = s.split(w, [None, 8]) | ||
c_o, c_i = s.split(c, [None, 32]) | ||
wio, wii = s.split(w_i, [None, 4]) | ||
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s.reorder(n, h_o, w_o, c_o, h_i, wio, wii, c_i) | ||
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return s |
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