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diff --git a/chapters/appendix_a.adoc b/chapters/appendix_a.adoc index f601d5d..33a4f11 100644 --- a/chapters/appendix_a.adoc +++ b/chapters/appendix_a.adoc @@ -30,257 +30,37 @@ float set_data(uint32_t set, uint32_t index) } ---- -=== Main Inference test data generator +=== Dot product floating-point test data sets -This section describes the function tosa_mi_data(S, KS, p, k, i) that generates test data for main inference compliance. -This function takes the following arguments: +Each test set is indexed by a pair (S, N) where: -* S is the test set number which identifies which generator is used -* KS is the kernel size -* p is the parameter number of 0 for the first input (usually data) and 1 for the second input (usually weights) -* k is the index within the kernel in the range 0 \<= k < KS -* i is the index within the tensor to write +* S is the test set number +* N is the number of elements in a single test vector -Some test data values are scaled by the bound parameter B which is defined in the table below. -B is set to be the largest value that is both representable by the input type and such that B*B does not overflow the accumulator precision. +Each test set (S, N) contains multiple tests that statistics are calculated over. +The parameter T is the number of tests in a given set. +In the table below, t is the test number within a set in the range 0 to T-1. +[cols="1,1,1,5,5"] |=== -| inputs type | accumulator type | B value -| fp16 | fp16 | (1<<8) - (1/8) = 255.875 -| fp16 | fp32 | (1<<16) - (1<<5) = 65504 -| bf16 | fp32 | (1<<64) - (1<<56) -| fp32 | fp32 | (1<<64) - (1<<40) -|=== - -==== Test set S=0 generator - -The aim of this generator is to check that sum of products with zero gives zero result. - -[cols="1,9"] -|=== -| p | tosa_mi_data(S, KS, p, k, i) = -| 0 | set_data(2*S, i) < 0 ? 0.0 : set_data(2*S+1, i) -| 1 | set_data(2*S, i) < 0 ? set_data(2*S+1, i) : 0.0 -|=== - -==== Test set S=1 - -The aim of this test set is to check values with large exponents. - -[cols="1,9"] -|=== -| p | tosa_mi_data(S, KS, p, k, i) = -| 0 | (B/sqrt(N))*(0.75 + 0.25*set_data(2*S+0, i)) -| 1 | (B/sqrt(N))*(0.75 + 0.25*set_data(2*S+1, i)) -|=== - -==== Test set S=2 - -The aim of this test set is to check rounding error when accumulating small values onto a large value. -In this case the small values are of similar magnitude. -If the implementation changes the order of the sum, then the test data must also be reordered so that the largest values occur first in the sum. - -[cols="1,9"] -|=== -| p | tosa_mi_data(S, KS, p, k, i) = -| 0 | (k==0) ? 1.0 : set_data(2*S+0, i)/sqrt(KS) -| 1 | (k==0) ? 1.0 : set_data(2*S+1, i)/sqrt(KS) -|=== - -==== Test set S=3 - -The aim of this test set is to check rounding error when accumulating small values onto a large value. -In this case the small values are of varying magnitude. -If the implementation changes the order of the sum, then the test data must also be reordered so that the largest values occur first in the sum. - -[cols="1,9"] -|=== -| p | tosa_mi_data(S, KS, p, k, i) = -| 0 | (k==0) ? 16.0 : exp(2*set_data(2*S+0, 2*i+0)) * set_data(2*S+0, 2*i+1) -| 1 | (k==0) ? 16.0 : exp(2*set_data(2*S+1, 2*i+0)) * set_data(2*S+1, 2*i+1) -|=== - -==== Test set S=4 - -The aim of this test set is to check a mixture of zero and non-zero products. - -[cols="1,9"] -|=== -| p | tosa_mi_data(S, KS, p, k, i) = -| 0 | (k==KS/2) ? +0.5 : (set_data(2*S, i) < 0 ? 0.0 : B*set_data(2*S+1, i)) -| 1 | (k==KS/2) ? -0.5 : (set_data(2*S, i) < 0 ? B*set_data(2*S+1, i) : 0.0) -|=== +| Set S | N range | T | x[k] formula for k < N | w[k] formula for k < N + +| 0 +| 2-25,50,100,1000 +| 10 +| x[k]=set_data(S, 2*t*N+2*k) < 0 ? 0.0 : set_data(S, 2*t*N+2*k+1) +| w[k]=set_data(S, 2*t*N+2*k) < 0 ? set_data(S, 2*t*N+2*k+1) : 0.0 + +| 1 +| 2-25,50,100,1000 +| 1000 +| x[k]=2.0*set_data(S, 2*t*N + k) +| w[k]=2.0*set_data(S, (2*t+1)*N + k) + +| 2 +| 2-25,50,100,1000 +| 1000 +| x[0]=1.0, x[k]=set_data(S, 2*t*N + k)/sqrt(N) for k>0 +| w[0]=1.0, w[k]=set_data(S, (2*t+1)*N + k)/sqrt(N) for k>0 -==== Test set S=5 - -The aim of this test set is to check signed inputs of large range. - -[cols="1,9"] |=== -| p | tosa_mi_data(S, KS, p, k, i) = -| 0 | (B/sqrt(KS))*set_data(2*S+0, i) -| 1 | (B/sqrt(KS))*set_data(2*S+1, i) -|=== - -=== Main Inference operator test data - -For each operator, this section defines how to generate test data for test set S. -For the results to be statistically significant the operation must calculate at least MIN_DOT_PRODUCTS dot products. -For most operations this means that the output tensor must have at least MIN_DOT_PRODUCTS output values. -For most operations batch size can be increased if necessary so that this holds. -For this version of the specification, MIN_DOT_PRODUCTS is set to 1000. - -==== CONV2D - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*OH*OW*OC >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = KW*KH*IC; -for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) { - input [ n, iy, ix, ic] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*IC+ic, ((n*IH+iy)*IW+ix)*IC+ic); -} -for (0 <= oc < OC, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) { - weight[oc, ky, kx, ic] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*IC+ic, ((oc*KH+ky)*KW+kx)*IC+ic); -} ----- - -==== CONV3D - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*OD*OH*OW*OC >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = KD*KW*KH*IC; -for (0 <= n < N, 0 <= id < UD, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) { - input [ n, id, iy, ix, ic] = tosa_mi_data(S, KS, 0, (((id % KD)*KH+(iy % KH))*KW+(ix % KW))*IC+ic, (((n*ID+id)*IH+iy)*IW+ix)*IC+ic); -} -for (0 <= oc < OC, 0 <= kd < KD, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) { - weight[oc, kd, ky, kx, ic] = tosa_mi_data(S, KS, 1, ((kd*KH+ky)*KW+kx)*IC+ic, (((oc*KD+kd)*KH+ky)*KW+kx)*IC+ic); -} ----- - -==== DEPTHWISE_CONV2D - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*OH*OW*C*M >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = KW*KH*C; -for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= c < C) { - input [ n, iy, ix, c] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*C+c, ((n*IH+iy)*IW+ix)*C+c); -} -for (0 <= ky < KH, 0 <= kx < KW, 0 <= c < C, 0 <= m < M) { - weight[ky, kx, c, m] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*C+c, ((ky*KW+kx)*C+c)*M+m); -} ----- - -==== FULLY_CONNECTED - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*OC >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = IC; -for (0 <= n < N, 0 <= ic < IC) { - input [ n, ic] = tosa_mi_data(S, KS, 0, ic, n*IC+ic); -} -for (0 <= oc < OC, 0 <= ic < IC) { - weight[oc, ic] = tosa_mi_data(S, KS, 1, ic, oc*IC+ic); -} ----- - -==== MATMUL - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*H*W >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = C; -for (0 <= n < N, 0 <= y < H, 0 <= c < C) { - A[n, y, c] = tosa_mi_data(S, KS, 0, c, (n*H+y)*C+c); -} -for (0 <= n < N, 0 <= c < C, 0 <= x < W) { - B[n, c, x] = tosa_mi_data(S, KS, 1, c, (n*C+c)*W+x); -} ----- - -==== TRANSPOSE_CONV2D - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*OH*OW*OC >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = KW*KH*IC; -for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) { - input [ n, iy, ix, ic] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*IC+ic, ((n*IH+iy)*IW+ix)*IC+ic); -} -for (0 <= oc < OC, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) { - weight[oc, ky, kx, ic] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*IC+ic, ((oc*KH+ky)*KW+kx)*IC+ic); -} ----- - -==== FFT2D - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*H*W >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = 2*H*W; -for (0 <= n < N, 0 <= y < H, 0 <= x < W) { - input_real[n, y, x] = tosa_mi_data(S, KS, 0, y*W+x, ((0*N+n)*H+y)*IW+x); - input_imag[n, y, x] = tosa_mi_data(S, KS, 0, y*W+x, ((1*N+n)*H+y)*IW+x); -} -for (0 <= y < H, 0 <= x < W, 0 <= m < H, 0 <= n < W) { - weight_real[y, x, m, n] = real(exp(2*pi*i*((m*h/H) + (n*w/W)))); - weight_imag[y, x, m, n] = imag(exp(2*pi*i*((m*h/H) + (n*w/W)))); -} ----- - -==== REDUCE_SUM - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`tensor_size(shape) >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = shape1[axis]; -for (index in shape1) { - input[index] = tosa_mi_data(S, KS, 0, index[axis], tensor_index_to_offset(index)); -} -for (0 <= c < KS) { - weight[c] = 1; -} ----- - -==== AVG_POOL2D - -The following generates input test data for test set S. -For compliant implementation, the test must pass whenever the attributes satisfy: -`N*OH*OW*C >= MIN_DOT_PRODUCTS` - -[source,c++] ----- -KS = KY*KX; -for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= c < C) { - input [ n, iy, ix, c] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*C+c, ((n*IH+iy)*IW+ix)*C+c); -} -for (0 <= ky < KH, 0 <= kx < KW, 0 <= c < C, 0 <= m < M) { - weight[ky, kx] = 1/KS; -} ----- |