From 32c0524815b32b04dde0d6b9a67d11083897a417 Mon Sep 17 00:00:00 2001 From: Austin Lund Date: Mon, 20 Jan 2025 16:17:02 +1000 Subject: [PATCH 1/7] Remove `julia` code and dependencies **Context:** Julia code was introduced previously to try and work around algorithmic instabilities in the computation of Fock coefficients from Gaussian Bargmann functions. This introduced a hard dependency on `julia`, which made installation tricker. In the end, it was found that the more stable approach was achieved by changing the algorithm to average over the possible lattice sites. **Description of the Change:** This change removes the `julia` code and the dependencies of that code. The PRECISION_BITS_HERMITE_POLY setting is removed, as that was the switch to turn on the `julia` implementation. The `julia` code has been pushed into the branch which stores implementations of this function. **Benefits:** There is no hard dependency on `juliacall` and the `julia` language. This should ease installation, particularly on Windows platforms. **Possible Drawbacks:** The ability switch to using the `julia` implementation is gone without a setting to bring it back quickly. Signed-off-by: Anthony --- .github/CHANGELOG.md | 1 + .github/workflows/builds.yml | 5 - .github/workflows/tests_numpy.yml | 8 - .github/workflows/tests_tensorflow.yml | 8 - .github/workflows/upload.yml | 5 - README.md | 7 - env.Dockerfile | 4 - mrmustard/juliapkg.json | 6 - mrmustard/math/backend_numpy.py | 21 +- mrmustard/math/backend_tensorflow.py | 102 +- .../julia/compactFock/diagonal_amps.jl | 148 -- .../julia/compactFock/diagonal_grad.jl | 195 -- .../julia/compactFock/helperFunctions.jl | 38 - .../compactFock/singleLeftoverMode_amps.jl | 231 --- .../compactFock/singleLeftoverMode_grad.jl | 302 --- .../lattice/strategies/julia/getPrecision.jl | 18 - .../math/lattice/strategies/julia/vanilla.jl | 82 - mrmustard/math/lattice/strategies/vanilla.jl | 73 - mrmustard/utils/settings.py | 41 - poetry.lock | 1629 ++++++++--------- pyproject.toml | 1 - tests/test_math/test_compactFock.py | 155 +- tests/test_math/test_lattice.py | 20 +- tests/test_utils/test_settings.py | 9 - 24 files changed, 898 insertions(+), 2211 deletions(-) delete mode 100644 mrmustard/juliapkg.json delete mode 100644 mrmustard/math/lattice/strategies/julia/compactFock/diagonal_amps.jl delete mode 100644 mrmustard/math/lattice/strategies/julia/compactFock/diagonal_grad.jl delete mode 100644 mrmustard/math/lattice/strategies/julia/compactFock/helperFunctions.jl delete mode 100644 mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_amps.jl delete mode 100644 mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_grad.jl delete mode 100644 mrmustard/math/lattice/strategies/julia/getPrecision.jl delete mode 100644 mrmustard/math/lattice/strategies/julia/vanilla.jl delete mode 100644 mrmustard/math/lattice/strategies/vanilla.jl diff --git a/.github/CHANGELOG.md b/.github/CHANGELOG.md index 25a85281f..60c0b5c8c 100644 --- a/.github/CHANGELOG.md +++ b/.github/CHANGELOG.md @@ -13,6 +13,7 @@ [(#506)](https://github.com/XanaduAI/MrMustard/pull/506) ### Breaking changes +* Remove julia code and dependencies. PRECISION_BITS_HERMITE_POLY setting removed. ### Improvements * Switch from the `julia` Python package to `juliacall` for easier installation and usage. diff --git a/.github/workflows/builds.yml b/.github/workflows/builds.yml index 3e08c7c35..e6b597077 100644 --- a/.github/workflows/builds.yml +++ b/.github/workflows/builds.yml @@ -45,10 +45,5 @@ jobs: - name: Install only test dependencies run: poetry install --no-root --extras "ray" --with dev - - name: Setup Julia - uses: julia-actions/setup-julia@v1 - with: - version: 1.9.3 - - name: Run tests run: python -m pytest tests -p no:warnings --tb=native --backend=${{ matrix.backend }} diff --git a/.github/workflows/tests_numpy.yml b/.github/workflows/tests_numpy.yml index 17f2689d3..466ca5995 100644 --- a/.github/workflows/tests_numpy.yml +++ b/.github/workflows/tests_numpy.yml @@ -29,11 +29,6 @@ jobs: with: python-version: ${{ matrix.python-version }} - - name: Setup Julia - uses: julia-actions/setup-julia@v1 - with: - version: 1.9.3 - - name: Install dependencies run: | python -m pip install --no-cache-dir --upgrade pip @@ -41,9 +36,6 @@ jobs: poetry config virtualenvs.create false poetry install --extras "ray" --with dev - - name: Initialize Julia project # to keep test times stable - run: python -c 'from juliacall import Main as jl' - - name: Create durations file name id: record_file run: echo "filename=durations_$(date +%s)_$(git rev-parse --short HEAD).txt" >> "$GITHUB_OUTPUT" diff --git a/.github/workflows/tests_tensorflow.yml b/.github/workflows/tests_tensorflow.yml index 2694e4e62..da68c6603 100644 --- a/.github/workflows/tests_tensorflow.yml +++ b/.github/workflows/tests_tensorflow.yml @@ -29,11 +29,6 @@ jobs: with: python-version: ${{ matrix.python-version }} - - name: Setup Julia - uses: julia-actions/setup-julia@v1 - with: - version: 1.9.3 - - name: Install dependencies run: | python -m pip install --no-cache-dir --upgrade pip @@ -41,9 +36,6 @@ jobs: poetry config virtualenvs.create false poetry install --extras "ray" --with dev - - name: Initialize Julia project # to keep test times stable - run: python -c 'from juliacall import Main as jl' - - name: Create durations file name id: record_file run: echo "filename=durations_$(date +%s)_$(git rev-parse --short HEAD).txt" >> "$GITHUB_OUTPUT" diff --git a/.github/workflows/upload.yml b/.github/workflows/upload.yml index 44269ba83..1a06cbeb3 100644 --- a/.github/workflows/upload.yml +++ b/.github/workflows/upload.yml @@ -33,11 +33,6 @@ jobs: - name: Install only test dependencies run: poetry install --no-root --extras "ray" --with dev - - name: Setup Julia - uses: julia-actions/setup-julia@v1 - with: - version: 1.9.3 - - name: Run tests run: python -m pytest tests -p no:warnings --tb=native --backend=tensorflow diff --git a/README.md b/README.md index df2bb5f9a..0fc314c73 100644 --- a/README.md +++ b/README.md @@ -31,13 +31,6 @@ Mr Mustard supports: - Plug-and-play backends (`numpy` as default) - An abstraction layer `XPTensor` for seamless symplectic algebra (experimental) -# Increased numerical stability using Julia [optional] - -Converting phase space objects to Fock space can be numerically unstable due to accumulating floating point errors. -To resolve this, the conversion can be performed with extended-precision arithmetic. To use this feature, -an installation of [Julia](https://github.com/JuliaLang/juliaup#installation) is required (version 1.9.3 recommended). -If no valid version of Julia is found, it will be installed automatically before trying to run any Julia code. - # The lab module The lab module contains things you'd find in a lab: states, transformations, measurements, circuits. States can be used at the beginning of a circuit as well as at the end, in which case a state is interpreted as a measurement (a projection onto that state). Transformations are usually parametrized and map states to states. The action on states is differentiable with respect to the state and to the gate parameters. diff --git a/env.Dockerfile b/env.Dockerfile index f2aced105..044cf202e 100644 --- a/env.Dockerfile +++ b/env.Dockerfile @@ -24,10 +24,6 @@ RUN python -m pip install --no-cache-dir --upgrade pip # Install all dependencies RUN poetry install --no-root --all-extras --with dev,doc -# Install Julia -COPY juliapkg.json . -RUN poetry run python -c 'from juliacall import Main' - ENV DEBIAN_FRONTEND=dialog # Add source code, tests and configuration diff --git a/mrmustard/juliapkg.json b/mrmustard/juliapkg.json deleted file mode 100644 index c04af7dee..000000000 --- a/mrmustard/juliapkg.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "julia": "~1.9.3", - "packages": { - "MultiFloats": {"uuid": "bdf0d083-296b-4888-a5b6-7498122e68a5"} - } -} diff --git a/mrmustard/math/backend_numpy.py b/mrmustard/math/backend_numpy.py index 434a682a8..6fcc91b61 100644 --- a/mrmustard/math/backend_numpy.py +++ b/mrmustard/math/backend_numpy.py @@ -518,23 +518,10 @@ def hermite_renormalized( The renormalized Hermite polynomial of given shape. """ - precision_bits = settings.PRECISION_BITS_HERMITE_POLY - - if precision_bits == 128: # numba - if settings.STABLE_FOCK_CONVERSION: - G = vanilla_stable(tuple(shape), A, b, c) - else: - G = vanilla(tuple(shape), A, b, c) - else: # julia (with precision_bits = 512) - # The following import must come after running "jl = Julia(compiled_modules=False)" in settings.py - from juliacall import Main as jl # pylint: disable=import-outside-toplevel - - A, b, c = ( - np.array(A, dtype=np.complex128), - np.array(b, dtype=np.complex128), - np.array(c, dtype=np.complex128), - ) - G = jl.Vanilla.vanilla(A, b, c.item(), np.array(shape, dtype=np.int64), precision_bits) + if settings.STABLE_FOCK_CONVERSION: + G = vanilla_stable(tuple(shape), A, b, c) + else: + G = vanilla(tuple(shape), A, b, c) return G diff --git a/mrmustard/math/backend_tensorflow.py b/mrmustard/math/backend_tensorflow.py index 474bd9760..027f3a552 100644 --- a/mrmustard/math/backend_tensorflow.py +++ b/mrmustard/math/backend_tensorflow.py @@ -454,28 +454,12 @@ def hermite_renormalized( The renormalized Hermite polynomial of given shape. """ - precision_bits = settings.PRECISION_BITS_HERMITE_POLY - A, b, c = self.asnumpy(A), self.asnumpy(b), self.asnumpy(c) - if precision_bits == 128: # numba - if settings.STABLE_FOCK_CONVERSION: - G = strategies.vanilla_stable(tuple(shape), A, b, c) - else: - G = strategies.vanilla(tuple(shape), A, b, c) - else: # julia - # The following import must come after settings settings.PRECISION_BITS_HERMITE_POLY - from juliacall import Main as jl # pylint: disable=import-outside-toplevel - - A, b, c = ( - A.astype(np.complex128), - b.astype(np.complex128), - c.astype(np.complex128), - ) - - G = self.astensor( - jl.Vanilla.vanilla(A, b, c.item(), np.array(shape, dtype=np.int64), precision_bits) - ) + if settings.STABLE_FOCK_CONVERSION: + G = strategies.vanilla_stable(tuple(shape), A, b, c) + else: + G = strategies.vanilla(tuple(shape), A, b, c) def grad(dLdGconj): dLdA, dLdB, dLdC = strategies.vanilla_vjp(G, c, np.conj(dLdGconj)) @@ -587,35 +571,17 @@ def hermite_renormalized_diagonal_reorderedAB( The renormalized Hermite polynomial. """ A, B, C = self.asnumpy(A), self.asnumpy(B), self.asnumpy(C) - precision_bits = settings.PRECISION_BITS_HERMITE_POLY - if precision_bits == 128: # numba (complex128) - poly0, poly2, poly1010, poly1001, poly1 = tf.numpy_function( - hermite_multidimensional_diagonal, [A, B, C, cutoffs], [A.dtype] * 5 - ) - else: # julia (higher precision than complex128) - # The following import must come after settings settings.PRECISION_BITS_HERMITE_POLY - from juliacall import Main as jl # pylint: disable=import-outside-toplevel - - (poly0, poly2, poly1010, poly1001, poly1) = ( - self.asnumpy(val) - for val in jl.DiagonalAmps.fock_diagonal_amps( - A, B, C.item(), tuple(cutoffs), precision_bits - ) - ) + poly0, poly2, poly1010, poly1001, poly1 = tf.numpy_function( + hermite_multidimensional_diagonal, [A, B, C, cutoffs], [A.dtype] * 5 + ) def grad(dLdpoly): - if precision_bits == 128: # numba (complex128) - dpoly_dC, dpoly_dA, dpoly_dB = tf.numpy_function( - grad_hermite_multidimensional_diagonal, - [A, B, C.item(), poly0, poly2, poly1010, poly1001, poly1], - [poly0.dtype] * 3, - ) - else: # julia (higher precision than complex128) - dpoly_dC = poly0 / C.item() - dpoly_dA, dpoly_dB = jl.DiagonalGrad.fock_diagonal_grad( - A, B, poly0, poly2, poly1010, poly1001, poly1, precision_bits - ) + dpoly_dC, dpoly_dA, dpoly_dB = tf.numpy_function( + grad_hermite_multidimensional_diagonal, + [A, B, C.item(), poly0, poly2, poly1010, poly1001, poly1], + [poly0.dtype] * 3, + ) ax = tuple(range(dLdpoly.ndim)) dLdA = self.sum(dLdpoly[..., None, None] * self.conj(dpoly_dA), axis=ax) @@ -685,43 +651,19 @@ def hermite_renormalized_1leftoverMode_reorderedAB( The renormalized Hermite polynomial. """ A, B, C = self.asnumpy(A), self.asnumpy(B), self.asnumpy(C) - precision_bits = settings.PRECISION_BITS_HERMITE_POLY - if precision_bits == 128: # numba (complex128) - poly0, poly2, poly1010, poly1001, poly1 = tf.numpy_function( - hermite_multidimensional_1leftoverMode, - [A, B, C.item(), cutoffs], - [A.dtype] * 5, - ) - else: # julia (higher precision than complex128) - # The following import must come after settings settings.PRECISION_BITS_HERMITE_POLY - from juliacall import Main as jl # pylint: disable=import-outside-toplevel - - ( - poly0, - poly2, - poly1010, - poly1001, - poly1, - ) = ( - self.asnumpy(val) - for val in jl.LeftoverModeAmps.fock_1leftoverMode_amps( - A, B, C.item(), tuple(cutoffs), precision_bits - ) - ) + poly0, poly2, poly1010, poly1001, poly1 = tf.numpy_function( + hermite_multidimensional_1leftoverMode, + [A, B, C.item(), cutoffs], + [A.dtype] * 5, + ) def grad(dLdpoly): - if precision_bits == 128: # numba (complex128) - dpoly_dC, dpoly_dA, dpoly_dB = tf.numpy_function( - grad_hermite_multidimensional_1leftoverMode, - [A, B, C, poly0, poly2, poly1010, poly1001, poly1], - [poly0.dtype] * 3, - ) - else: # julia (higher precision than complex128) - dpoly_dC = poly0 / C.item() - (dpoly_dA, dpoly_dB) = jl.LeftoverModeGrad.fock_1leftoverMode_grad( - A, B, poly0, poly2, poly1010, poly1001, poly1, precision_bits - ) + dpoly_dC, dpoly_dA, dpoly_dB = tf.numpy_function( + grad_hermite_multidimensional_1leftoverMode, + [A, B, C, poly0, poly2, poly1010, poly1001, poly1], + [poly0.dtype] * 3, + ) ax = tuple(range(dLdpoly.ndim)) dLdA = self.sum(dLdpoly[..., None, None] * self.conj(dpoly_dA), axis=ax) diff --git a/mrmustard/math/lattice/strategies/julia/compactFock/diagonal_amps.jl b/mrmustard/math/lattice/strategies/julia/compactFock/diagonal_amps.jl deleted file mode 100644 index 3c0d82fb5..000000000 --- a/mrmustard/math/lattice/strategies/julia/compactFock/diagonal_amps.jl +++ /dev/null @@ -1,148 +0,0 @@ -module DiagonalAmps - -import ..GetPrecision -import ..CompactFock_HelperFunctions - -function use_offDiag_pivot!(A, B, M, cutoffs, params, d, arr0, arr2, arr1010, arr1001, arr1, T, SQRT) - """Given params=(a,b,c,...), apply the recurrence relation for the pivots - [a+1,a,b,b,c,c,...] / [a,a,b+1,b,c,c,...] / [a,a,b,b,c+1,c,...] / ...""" - pivot = CompactFock_HelperFunctions.repeat_twice(params) - pivot[2 * d - 1] += 1 - - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - - G_in = zeros(Complex{T}, 2 * M) - - ########## READ ########## - GB = arr1[2 * d - 1, params...] .* B - - # Array0 - G_in[2 * d - 1] = arr0[params...] - - # read from Array2 - if params[d] > 1 - params_adapted = collect(params) - params_adapted[d] -= 1 - G_in[2 * d] = arr2[d, params_adapted...] - end - - # read from Array11 - for i in d+1:M # i>d - if params[i] > 1 - params_adapted = collect(params) - params_adapted[i] -= 1 - G_in[2 * i - 1] = arr1001[d, i - d, params_adapted...] - G_in[2 * i] = arr1010[d, i - d, params_adapted...] - end - end - - ########## WRITE ########## - G_in .*= K_l - - # Array0 - params_adapted = collect(params) - params_adapted[d] += 1 - arr0[params_adapted...] = (GB[2 * d] .+ sum(A[2 * d,:] .* G_in)) / K_i[2 * d] - - # Array2 - if params[d] + 1 < cutoffs[d] - arr2[d, params...] = (GB[2 * d - 1] .+ sum(A[2 * d - 1,:] .* G_in)) / K_i[2 * d - 1] - end - - # Array11 - for i in d+1:M # i>d - if params[i] < cutoffs[i] - arr1010[d, i - d, params...] = (GB[2 * i - 1] .+ sum(A[2 * i - 1,:] .* G_in)) / K_i[2 * i - 1] - arr1001[d, i - d, params...] = (GB[2 * i] .+ sum(A[2 * i,:] .* G_in)) / K_i[2 * i] - end - end -end - -function use_diag_pivot!(A, B, M, cutoffs, params, arr0, arr1, T, SQRT) - """Given params=(a,b,c,...), apply the recurrence relation for the pivot [a,a,b,b,c,c...]""" - pivot = CompactFock_HelperFunctions.repeat_twice(params) - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, 2*M) - - ########## READ ########## - GB = arr0[params...] .* B - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] > 1 - params_adapted = collect(params) - params_adapted[(i-1)÷2+1] -= 1 - G_in[i] = arr1[i+1-2*((i-1) % 2), params_adapted...] - end - end - - ########## WRITE ########## - G_in .*= K_l - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] < cutoffs[(i-1)÷2+1] - # this prevents a few elements from being written that will never be read - if i ≠ 2 || params[1] + 1 < cutoffs[1] - arr1[i, params...] = (GB[i] .+ sum(A[i,:] .* G_in)) / K_i[i] - end - end - end -end - -function fock_diagonal_amps( - A::AbstractMatrix{Complex{Float64}}, - B::AbstractVector{Complex{Float64}}, - G0::Complex{Float64}, - cutoffs::Tuple, - precision_bits::Int64 - ) - """Returns the PNR probabilities of a mixed state according to algorithm 1 of - https://doi.org/10.22331/q-2023-08-29-1097 - Args: - A, B, G0: required input for recurrence relation - cutoffs: upper bounds for the number of photons in each mode - precision_bits: number of bits used to represent a single Fock amplitude - Returns: - Submatrices of the Fock representation. Each submatrix contains Fock indices of a certain type. - arr0 --> type: [a,a,b,b,c,c...] - arr2 --> type: [a+2,a,b,b,c,c...] / [a,a,b+2,b,c,c...] / ... - arr1010 --> type: [a+1,a,b+1,b,c,c,...] / [a+1,a,b,b,c+1,c,...] / [a,a,b+1,b,c+1,c,...] / ... - arr1001 --> type: [a+1,a,b,b+1,c,c,...] / [a+1,a,b,b,c,c+1,...] / [a,a,b+1,b,c,c+1,...] / ... - arr1 --> type: [a+1,a,b,b,c,c...] / [a,a+1,b,b,c,c...] / [a,a,b+1,b,c,c...] / ... - """ - - T = GetPrecision.get_dtype(precision_bits) - SQRT = GetPrecision.SQRT_dict[precision_bits] - - M = length(cutoffs) - - arr0 = zeros(Complex{T}, cutoffs) - arr0[fill(1,M)...] = G0 - arr2 = zeros(Complex{T}, M, cutoffs...) - arr1 = zeros(Complex{T}, 2*M, cutoffs...) - arr1010 = zeros(Complex{T}, M, M - 1, cutoffs...) - arr1001 = zeros(Complex{T}, M, M - 1, cutoffs...) - - - dict_params = CompactFock_HelperFunctions.construct_dict_params(cutoffs) - for sum_params in 0:sum(cutoffs)-1 - for params in dict_params[sum_params] - # diagonal pivots: aa,bb,cc,... - if (cutoffs[1] == 1) || (params[1] < cutoffs[1]) # julia indexing! - use_diag_pivot!(A, B, M, cutoffs, params, arr0, arr1, T, SQRT) - end - # off-diagonal pivots: d=1: (a+1)a,bb,cc,... | d=2: 00,(b+1)b,cc,... | d=3: 00,00,(c+1)c,... | ... - for d in 1:M - if all(params[1:d-1] .== 1) && (params[d] < cutoffs[d]) # julia indexing! - use_offDiag_pivot!(A, B, M, cutoffs, params, d, arr0, arr2, arr1010, arr1001, arr1, T, SQRT) - end - end - end - end - return Complex{Float64}.(arr0), Complex{Float64}.(arr2), Complex{Float64}.(arr1010), Complex{Float64}.(arr1001), Complex{Float64}.(arr1) -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/julia/compactFock/diagonal_grad.jl b/mrmustard/math/lattice/strategies/julia/compactFock/diagonal_grad.jl deleted file mode 100644 index 9dfce8a0d..000000000 --- a/mrmustard/math/lattice/strategies/julia/compactFock/diagonal_grad.jl +++ /dev/null @@ -1,195 +0,0 @@ -module DiagonalGrad - -import ..GetPrecision -import ..CompactFock_HelperFunctions - -function calc_dA_dB(i, G_in_dA, G_in_dB, G_in, A, B, K_l, K_i, M, pivot_val, pivot_val_dA, pivot_val_dB) - """Calculate the derivatives of a single Fock amplitude w.r.t A and B. - Args: - i (int): the element of the multidim index that is increased - G_in, G_in_dA, G_in_dB (array, array, array): all Fock amplitudes from the 'read' group in the recurrence relation and their derivatives w.r.t. A and B - A, B (array, vector): required input for recurrence relation (given by mrmustard.physics.fock_utils.ABC) - K_l, K_i (vector, vector): SQRT[pivot], SQRT[pivot + 1] - M (int): number of modes - pivot_val, pivot_val_dA, pivot_val_dB (array, array, array): Fock amplitude at the position of the pivot and its derivatives w.r.t. A and B - """ - dA = pivot_val_dA .* B[i] - dB = pivot_val_dB .* B[i] - dB[i] += pivot_val - for l in 1:2*M - dA += K_l[l] * A[i, l] * G_in_dA[l,:,:] - dB += K_l[l] * A[i, l] * G_in_dB[l,:] - dA[i, l] += G_in[l] - end - return dA ./ K_i[i], dB ./ K_i[i] -end - -function use_offDiag_pivot_grad!(A, B, M, cutoffs, params, d, arr0, arr2, arr1010, arr1001, arr1, - arr0_dA, arr2_dA, arr1010_dA, arr1001_dA, arr1_dA, arr0_dB, arr2_dB, arr1010_dB, arr1001_dB, arr1_dB, T, SQRT) - """Given params=(a,b,c,...), apply the eqs. 16 & 17 (of https://doi.org/10.22331/q-2023-08-29-1097) - for the pivots [a+1,a,b,b,c,c,...] / [a,a,b+1,b,c,c,...] / [a,a,b,b,c+1,c,...] / ...""" - - pivot = CompactFock_HelperFunctions.repeat_twice(params) - pivot[2 * d - 1] += 1 - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, 2 * M) - G_in_dA = zeros(Complex{T}, 2*M, size(A)...) - G_in_dB = zeros(Complex{T}, 2*M, size(B)...) - - ########## READ ########## - pivot_val = arr1[2 * d - 1, params...] - pivot_val_dA = arr1_dA[2 * d - 1, params...,:,:] - pivot_val_dB = arr1_dB[2 * d - 1, params...,:] - - # Array0 - G_in[2 * d - 1] = arr0[params...] - G_in_dA[2 * d - 1,:,:] = arr0_dA[params...,:,:] - G_in_dB[2 * d - 1,:] = arr0_dB[params...,:] - - # read from Array2 - if params[d] > 1 - params_adapted = collect(params) - params_adapted[d] -= 1 - G_in[2 * d] = arr2[d, params_adapted...] - G_in_dA[2 * d,:,:] = arr2_dA[d, params_adapted...,:,:] - G_in_dB[2 * d,:] = arr2_dB[d, params_adapted...,:] - end - - # read from Array11 - for i in d+1:M # i>d - if params[i] > 1 - params_adapted = collect(params) - params_adapted[i] -= 1 - G_in[2 * i - 1] = arr1001[d, i - d, params_adapted...] - G_in_dA[2 * i - 1,:,:] = arr1001_dA[d, i - d, params_adapted...,:,:] - G_in_dB[2 * i - 1,:] = arr1001_dB[d, i - d, params_adapted...,:] - G_in[2 * i] = arr1010[d, i - d, params_adapted...] - G_in_dA[2 * i,:,:] = arr1010_dA[d, i - d, params_adapted...,:,:] - G_in_dB[2 * i,:] = arr1010_dB[d, i - d, params_adapted...,:] - end - end - - ########## WRITE ########## - G_in .*= K_l - - # Array0 - params_adapted = collect(params) - params_adapted[d] += 1 - arr0_dA[params_adapted...,:,:], arr0_dB[params_adapted...,:] = calc_dA_dB(2 * d, G_in_dA, G_in_dB, G_in, A, B, K_l, K_i, M, pivot_val, pivot_val_dA, pivot_val_dB) - - # Array2 - if params[d] + 1 < cutoffs[d] - arr2_dA[d, params...,:,:], arr2_dB[d, params...,:] = calc_dA_dB(2 * d - 1, G_in_dA, G_in_dB, G_in, A, B, K_l, K_i, M, pivot_val, pivot_val_dA, pivot_val_dB) - end - - # Array11 - for i in d+1:M # i>d - if params[i] < cutoffs[i] - arr1010_dA[d, i - d, params...,:,:], arr1010_dB[d, i - d, params...,:] = calc_dA_dB(2 * i - 1, G_in_dA, G_in_dB, G_in, A, B, K_l, K_i, M, pivot_val, pivot_val_dA, pivot_val_dB) - arr1001_dA[d, i - d, params...,:,:], arr1001_dB[d, i - d, params...,:] = calc_dA_dB(2 * i, G_in_dA, G_in_dB, G_in, A, B, K_l, K_i, M, pivot_val, pivot_val_dA, pivot_val_dB) - end - end -end -function use_diag_pivot_grad!(A, B, M, cutoffs, params, arr0, arr1, arr0_dA, arr1_dA, arr0_dB, arr1_dB, T, SQRT) - """Given params=(a,b,c,...), apply the eqs. 16 & 17 (of https://doi.org/10.22331/q-2023-08-29-1097) - for the pivot [a,a,b,b,c,c...]""" - pivot = CompactFock_HelperFunctions.repeat_twice(params) - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, 2*M) - G_in_dA = zeros(Complex{T}, 2*M, size(A)...) - G_in_dB = zeros(Complex{T}, 2*M, size(B)...) - - ########## READ ########## - pivot_val = arr0[params...] - pivot_val_dA = arr0_dA[params...,:,:] - pivot_val_dB = arr0_dB[params...,:] - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] > 1 - i_staggered = i+1-2*((i-1) % 2) - params_adapted = collect(params) - params_adapted[(i-1)÷2+1] -= 1 - G_in[i] = arr1[i_staggered, params_adapted...] - G_in_dA[i,:,:] = arr1_dA[i_staggered, params_adapted...,:,:] - G_in_dB[i,:] = arr1_dB[i_staggered, params_adapted...,:] - end - end - - ########## WRITE ########## - G_in .*= K_l - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] < cutoffs[(i-1)÷2+1] - # This if statement prevents a few elements from being written that will never be read - if i ≠ 2 || params[1] + 1 < cutoffs[1] - arr1_dA[i, params...,:,:], arr1_dB[i, params...,:] = calc_dA_dB(i, G_in_dA, G_in_dB, G_in, A, B, K_l, K_i, M, pivot_val, pivot_val_dA, pivot_val_dB) - end - end - end -end -function fock_diagonal_grad( - A::AbstractMatrix{Complex{Float64}}, - B::AbstractVector{Complex{Float64}}, - arr0::AbstractArray{Complex{Float64}}, - arr2::AbstractArray{Complex{Float64}}, - arr1010::AbstractArray{Complex{Float64}}, - arr1001::AbstractArray{Complex{Float64}}, - arr1::AbstractArray{Complex{Float64}}, - precision_bits::Int64 - ) - """Returns the gradients of the PNR probabilities of a mixed state according to algorithm 1 of - https://doi.org/10.22331/q-2023-08-29-1097 - Args: - A, B: required input for recurrence relation - Submatrices of the Fock representation. Each submatrix contains Fock indices of a certain type. - arr0 --> type: [a,a,b,b,c,c...] - arr2 --> type: [a+2,a,b,b,c,c...] / [a,a,b+2,b,c,c...] / ... - arr1010 --> type: [a+1,a,b+1,b,c,c,...] / [a+1,a,b,b,c+1,c,...] / [a,a,b+1,b,c+1,c,...] / ... - arr1001 --> type: [a+1,a,b,b+1,c,c,...] / [a+1,a,b,b,c,c+1,...] / [a,a,b+1,b,c,c+1,...] / ... - arr1 --> type: [a+1,a,b,b,c,c...] / [a,a+1,b,b,c,c...] / [a,a,b+1,b,c,c...] / ... - precision_bits: number of bits used to represent a single Fock amplitude - Returns: - arr0_dA, arr0_dB: derivatives of arr0 w.r.t A and B - """ - - T = GetPrecision.get_dtype(precision_bits) - SQRT = GetPrecision.SQRT_dict[precision_bits] - - cutoffs = size(arr0) - M = length(cutoffs) - - arr0_dA = zeros(Complex{T}, size(arr0)..., size(A)...) - arr2_dA = zeros(Complex{T}, size(arr2)..., size(A)...) - arr1010_dA = zeros(Complex{T}, size(arr1010)..., size(A)...) - arr1001_dA = zeros(Complex{T}, size(arr1001)..., size(A)...) - arr1_dA = zeros(Complex{T}, size(arr1)..., size(A)...) - arr0_dB = zeros(Complex{T}, size(arr0)..., size(B)...) - arr2_dB = zeros(Complex{T}, size(arr2)..., size(B)...) - arr1010_dB = zeros(Complex{T}, size(arr1010)..., size(B)...) - arr1001_dB = zeros(Complex{T}, size(arr1001)..., size(B)...) - arr1_dB = zeros(Complex{T}, size(arr1)..., size(B)...) - - dict_params = CompactFock_HelperFunctions.construct_dict_params(cutoffs) - for sum_params in 0:sum(cutoffs)-1 - for params in dict_params[sum_params] - # diagonal pivots: aa,bb,cc,... - if (cutoffs[1] == 1) || (params[1] < cutoffs[1]) # julia indexing! - use_diag_pivot_grad!(A, B, M, cutoffs, params, arr0, arr1, arr0_dA, arr1_dA, arr0_dB, arr1_dB, T, SQRT) - end - # off-diagonal pivots: d=1: (a+1)a,bb,cc,... | d=2: 00,(b+1)b,cc,... | d=3: 00,00,(c+1)c,... | ... - for d in 1:M - if all(params[1:d-1] .== 1) && (params[d] < cutoffs[d]) # julia indexing! - use_offDiag_pivot_grad!(A,B,M,cutoffs,params,d,arr0,arr2,arr1010,arr1001,arr1,arr0_dA,arr2_dA,arr1010_dA,arr1001_dA,arr1_dA,arr0_dB,arr2_dB,arr1010_dB,arr1001_dB,arr1_dB, T, SQRT) - end - end - end - end - - return Complex{Float64}.(arr0_dA), Complex{Float64}.(arr0_dB) -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/julia/compactFock/helperFunctions.jl b/mrmustard/math/lattice/strategies/julia/compactFock/helperFunctions.jl deleted file mode 100644 index 1b8847855..000000000 --- a/mrmustard/math/lattice/strategies/julia/compactFock/helperFunctions.jl +++ /dev/null @@ -1,38 +0,0 @@ -module CompactFock_HelperFunctions - -function repeat_twice(params) - """ - Args: - params: [a,b,c,...] - Returns: - [a,a,b,b,c,c,...] - """ - pivot = Vector{Int64}(undef, 2 * length(params)) - for (i, val) in enumerate(params) - pivot[2 * i - 1] = val - pivot[2 * i] = val - end - return pivot -end - -function construct_dict_params(cutoffs) - """ - Args: - cutoffs (tuple): upper bounds for the number of photons in each mode - Returns: - Dict: all possible values for (a,b,c,...), grouped in lists according to their sum a+b+c+... - """ - M = length(cutoffs) - indices = Dict{Int64, Vector{Tuple}}() - for sum_params in 0:sum(cutoffs)-1 - indices[sum_params] = Vector{Tuple}() - end - - for params in CartesianIndices(cutoffs) - params_tup = Tuple(params) - push!(indices[sum(params_tup) - M], params_tup) - end - return indices -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_amps.jl b/mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_amps.jl deleted file mode 100644 index 63efe46e5..000000000 --- a/mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_amps.jl +++ /dev/null @@ -1,231 +0,0 @@ -module LeftoverModeAmps - -import ..GetPrecision -import ..CompactFock_HelperFunctions - -function write_block!(i, arr_write, write, arr_read_pivot, read_GB, G_in, GB, A, K_i, cutoff_leftoverMode, SQRT) - """ - Apply the recurrence relation to blocks of Fock amplitudes (of shape cutoff_leftoverMode x cutoff_leftoverMode) - (cfr. algorithm 2 of https://doi.org/10.22331/q-2023-08-29-1097) - """ - m, n = 1, 1 - A_adapted = A[i, 3:end] - G_in_adapted = G_in[1, 1, :] - arr_write[1, 1, write...] = (GB[1, 1, i] + sum(A_adapted .* G_in_adapted)) / K_i[i - 2] - - m = 1 - A_adapted = A[i, 2:end] - for n in 2:cutoff_leftoverMode - G_in_adapted = vcat(arr_read_pivot[1, n - 1, read_GB...] * SQRT[n], G_in[1, n, :]) - arr_write[1, n, write...] = (GB[1, n, i] + sum(A_adapted .* G_in_adapted)) / K_i[i - 2] - end - - n = 1 - A_adapted = vcat(A[i, 1], A[i, 3:end]) - for m in 2:cutoff_leftoverMode - G_in_adapted = vcat(arr_read_pivot[m - 1, 1, read_GB...] * SQRT[m], G_in[m, 1, :]) - arr_write[m, 1, write...] = (GB[m, 1, i] + sum(A_adapted .* G_in_adapted)) / K_i[i - 2] - end - - A_adapted = A[i, :] - for m in 2:cutoff_leftoverMode - for n in 2:cutoff_leftoverMode - G_in_adapted = vcat(arr_read_pivot[m - 1, n, read_GB...] * SQRT[m], arr_read_pivot[m, n - 1, read_GB...] * SQRT[n], G_in[m, n, :]) - arr_write[m, n, write...] = (GB[m, n, i] + sum(A_adapted .* G_in_adapted)) / K_i[i - 2] - end - end -end - -function use_offDiag_pivot!(A, B, M, cutoff_leftoverMode, cutoffs_tail, params, d, arr0, arr2, arr1010, arr1001, arr1, T, SQRT) - """Given params=(a,b,c,...), apply the recurrence relation for the pivots - [a+1,a,b,b,c,c,...] / [a,a,b+1,b,c,c,...] / [a,a,b,b,c+1,c,...] / ...""" - pivot = CompactFock_HelperFunctions.repeat_twice(params) - pivot[2 * d - 1] += 1 - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, 2 * M) - - ########## READ ########## - read_GB = tuple(2 * d - 1, params...) - GB = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, length(B)) - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - GB[m, n, :] = arr1[m, n, read_GB...] .* B - end - end - - # Array0 - G_in[:, :, 2 * d - 1] .= arr0[:, :, params...] - - - # read from Array2 - if params[d] > 1 - params_adapted = collect(params) - params_adapted[d] -= 1 - G_in[:, :, 2 * d] .= arr2[:, :, d, params_adapted...] # read block - end - - # read from Array11 - for i in d+1:M # i>d - if params[i] > 1 - params_adapted = collect(params) - params_adapted[i] -= 1 - G_in[:, :, 2 * i - 1] .= arr1001[:, :, d, i - d, params_adapted...] # read block - G_in[:, :, 2 * i] .= arr1010[:, :, d, i - d, params_adapted...] # read block - end - end - - ########## WRITE ########## - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - G_in[m, n, :] .*= K_l - end - end - - # Array0 - write = collect(params) - write[d] += 1 - write_block!(2 * d + 2, arr0, write, arr1, read_GB, G_in, GB, A, K_i, cutoff_leftoverMode, SQRT) - - # Array2 - if params[d] + 1 < cutoffs_tail[d] - write = (d, params...) - write_block!(2 * d + 1, arr2, write, arr1, read_GB, G_in, GB, A, K_i, cutoff_leftoverMode, SQRT) - end - - # Array11 - for i in d+1:M - if params[i] < cutoffs_tail[i] - write = (d, i - d, params...) - write_block!(2 * i + 1, arr1010, write, arr1, read_GB, G_in, GB, A, K_i, cutoff_leftoverMode, SQRT) - write_block!(2 * i + 2, arr1001, write, arr1, read_GB, G_in, GB, A, K_i, cutoff_leftoverMode, SQRT) - end - end -end - -function use_diag_pivot!(A, B, M, cutoff_leftoverMode, cutoffs_tail, params, arr0, arr1, T, SQRT) - """Given params=(a,b,c,...), apply the recurrence relation for the pivot [a,a,b,b,c,c...]""" - pivot = CompactFock_HelperFunctions.repeat_twice(params) - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, 2*M) - - ########## READ ########## - read_GB = params - GB = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, length(B)) - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - GB[m, n, :] = arr0[m, n, read_GB...] .* B - end - end - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] > 1 - params_adapted = collect(params) - params_adapted[(i-1)÷2+1] -= 1 - G_in[:, :, i] .= arr1[:, :, i+1-2*((i-1) % 2), params_adapted...] # read block - end - end - - ########## WRITE ########## - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - G_in[m, n, :] .*= K_l - end - end - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] < cutoffs_tail[(i-1)÷2+1] - # this if statement prevents a few elements from being written that will never be read - if i ≠ 2 || params[1] + 1 < cutoffs_tail[1] - write = tuple(i,params...) - write_block!(i + 2, arr1, write, arr0, read_GB, G_in, GB, A, K_i, cutoff_leftoverMode, SQRT) - end - end - end -end - -function fill_firstMode_PNRzero!(arr0,A,B,M,cutoff_leftoverMode,SQRT) - # fill first mode for all PNR detections equal to zero - one_tuple = tuple(fill(1,M-1)...) - for m in 1:cutoff_leftoverMode-1 - arr0[m + 1, 1, one_tuple...] = (arr0[m, 1, one_tuple...] * B[1]) / SQRT[m + 1] - if m != 1 - arr0[m + 1, 1, one_tuple...] += (SQRT[m] * A[1, 1] * arr0[m - 1, 1, one_tuple...]) / SQRT[m + 1] - end - end - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode-1 - arr0[m, n + 1, one_tuple...] = (arr0[m, n, one_tuple...] * B[2]) / SQRT[n + 1] - if m != 1 - arr0[m, n + 1, one_tuple...] += (SQRT[m] * A[2, 1] * arr0[m - 1, n, one_tuple...]) / SQRT[n + 1] - end - if n != 1 - arr0[m, n + 1, one_tuple...] += (SQRT[n] * A[2, 2] * arr0[m, n - 1, one_tuple...]) / SQRT[n + 1] - end - end - end -end - -function fock_1leftoverMode_amps( - A::AbstractMatrix{Complex{Float64}}, - B::AbstractVector{Complex{Float64}}, - G0::Complex{Float64}, - cutoffs::Tuple, - precision_bits::Int64 - ) - """Returns the density matrices in the upper, undetected mode of a circuit when all other modes are PNR detected - according to algorithm 2 of https://doi.org/10.22331/q-2023-08-29-1097 - Args: - A, B, G0: required input for recurrence relation - cutoffs: upper bounds for the number of photons in each mode - precision_bits: number of bits used to represent a single Fock amplitude - Returns: - Submatrices of the Fock representation. Each submatrix contains Fock indices of a certain type. - arr0 --> type: [a,a,b,b,c,c...] - arr2 --> type: [a+2,a,b,b,c,c...] / [a,a,b+2,b,c,c...] / ... - arr1010 --> type: [a+1,a,b+1,b,c,c,...] / [a+1,a,b,b,c+1,c,...] / [a,a,b+1,b,c+1,c,...] / ... - arr1001 --> type: [a+1,a,b,b+1,c,c,...] / [a+1,a,b,b,c,c+1,...] / [a,a,b+1,b,c,c+1,...] / ... - arr1 --> type: [a+1,a,b,b,c,c...] / [a,a+1,b,b,c,c...] / [a,a,b+1,b,c,c...] / ... - """ - - T = GetPrecision.get_dtype(precision_bits) - SQRT = GetPrecision.SQRT_dict[precision_bits] - - M = length(cutoffs) - cutoff_leftoverMode = cutoffs[1] - cutoffs_tail = cutoffs[2:end] - - arr0 = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, cutoffs_tail...) - arr0[fill(1,M+1)...] = G0 - arr2 = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, M - 1, cutoffs_tail...) - arr1 = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, 2 * (M - 1), cutoffs_tail...) - arr1010 = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, M - 1, M - 2, cutoffs_tail...) - arr1001 = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, M - 1, M - 2, cutoffs_tail...) - - - # fill first mode for all PNR detections equal to zero - fill_firstMode_PNRzero!(arr0,A,B,M,cutoff_leftoverMode,SQRT) - - dict_params = CompactFock_HelperFunctions.construct_dict_params(cutoffs_tail) - for sum_params in 0:sum(cutoffs_tail)-1 - for params in dict_params[sum_params] - # diagonal pivots: aa,bb,cc,... - if (cutoffs_tail[1] == 1) || (params[1] < cutoffs_tail[1]) # julia indexing! - use_diag_pivot!(A, B, M - 1, cutoff_leftoverMode, cutoffs_tail, params, arr0, arr1, T, SQRT) - end - # off-diagonal pivots: d=1: (a+1)a,bb,cc,... | d=2: 00,(b+1)b,cc,... | d=3: 00,00,(c+1)c,... | ... - for d in 1:M - 1 - if all(params[1:d-1] .== 1) && (params[d] < cutoffs_tail[d]) - use_offDiag_pivot!(A,B,M - 1,cutoff_leftoverMode,cutoffs_tail,params,d,arr0,arr2,arr1010,arr1001,arr1,T,SQRT) - end - end - end - end - - return Complex{Float64}.(arr0), Complex{Float64}.(arr2), Complex{Float64}.(arr1010), Complex{Float64}.(arr1001), Complex{Float64}.(arr1) -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_grad.jl b/mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_grad.jl deleted file mode 100644 index bb8e6c6da..000000000 --- a/mrmustard/math/lattice/strategies/julia/compactFock/singleLeftoverMode_grad.jl +++ /dev/null @@ -1,302 +0,0 @@ -module LeftoverModeGrad - -import ..GetPrecision -import ..CompactFock_HelperFunctions - -function calc_dA_dB(m, n, i, arr_read_pivot, read_GB, G_in_adapted, A_adapted, B, K_i, K_l_adapted, arr_read_pivot_dA, G_in_dA_adapted, arr_read_pivot_dB, G_in_dB_adapted, l_range) - """Apply eqs. 16 & 17 (of https://doi.org/10.22331/q-2023-08-29-1097) for a single Fock amplitude""" - dA = arr_read_pivot_dA[m, n, read_GB...,:,:] .* B[i] - dB = arr_read_pivot_dB[m, n, read_GB...,:] .* B[i] - dB[i] += arr_read_pivot[m, n, read_GB...] - for (l_prime, l) in enumerate(l_range) - dA += (K_l_adapted[l_prime] * A_adapted[l_prime]) .* G_in_dA_adapted[l_prime,:,:] - dB += (K_l_adapted[l_prime] * A_adapted[l_prime]) .* G_in_dB_adapted[l_prime,:] - dA[i, l] += G_in_adapted[l_prime] - end - return dA ./ K_i[i - 2], dB ./ K_i[i - 2] -end - -function write_block_grad(i, write, arr_read_pivot, read_GB, G_in, A, B, K_i, K_l, cutoff_leftoverMode, arr_write_dA, arr_read_pivot_dA, G_in_dA, arr_write_dB, arr_read_pivot_dB, G_in_dB, SQRT) - """ - Apply eqs. 16 & 17 (of https://doi.org/10.22331/q-2023-08-29-1097) - to blocks of Fock amplitudes (of shape cutoff_leftoverMode x cutoff_leftoverMode) - """ - m, n = 1, 1 - l_range = 3:size(A)[2] - A_adapted = A[i, 3:end] - G_in_adapted = G_in[1, 1, :] - G_in_dA_adapted = G_in_dA[1,1,:,:,:] - G_in_dB_adapted = G_in_dB[1,1,:,:] - K_l_adapted = K_l - arr_write_dA[1, 1, write...,:,:], arr_write_dB[1, 1, write...,:] = calc_dA_dB(m, n, i, arr_read_pivot, read_GB, G_in_adapted, A_adapted, B, K_i, K_l_adapted, arr_read_pivot_dA, G_in_dA_adapted, arr_read_pivot_dB, G_in_dB_adapted, l_range) - - m = 1 - l_range = 2:size(A)[2] - A_adapted = A[i, 2:end] - for n in 2:cutoff_leftoverMode - K_l_adapted = vcat(SQRT[n], K_l) - G_in_adapted = vcat(arr_read_pivot[1, n - 1, read_GB...] * SQRT[n], G_in[1, n, :]) - G_in_dA_adapted = vcat(reshape(arr_read_pivot_dA[1, n - 1, read_GB...,:,:], (1, size(A)...)), G_in_dA[1,n,:,:,:]) - G_in_dB_adapted = vcat(reshape(arr_read_pivot_dB[1, n - 1, read_GB...,:], (1, size(B)...)), G_in_dB[1,n,:,:]) - arr_write_dA[1, n, write...,:,:], arr_write_dB[1, n, write...,:] = calc_dA_dB(m, n, i, arr_read_pivot, read_GB, G_in_adapted, A_adapted, B, K_i, K_l_adapted, arr_read_pivot_dA, G_in_dA_adapted, arr_read_pivot_dB, G_in_dB_adapted, l_range) - end - - n = 1 - l_range = [2:size(A)[2]...] - l_range[1] = 1 - A_adapted = vcat(A[i, 1], A[i, 3:end]) - for m in 2:cutoff_leftoverMode - K_l_adapted = vcat(SQRT[m], K_l) - G_in_adapted = vcat(arr_read_pivot[m - 1, 1, read_GB...] * SQRT[m], G_in[m, 1, :]) - G_in_dA_adapted = vcat(reshape(arr_read_pivot_dA[m - 1, 1, read_GB...,:,:], (1, size(A)...)), G_in_dA[m, 1,:,:,:]) - G_in_dB_adapted = vcat(reshape(arr_read_pivot_dB[m - 1, 1, read_GB...,:], (1, size(B)...)), G_in_dB[m, 1,:,:]) - arr_write_dA[m, 1, write...,:,:], arr_write_dB[m, 1, write...,:] = calc_dA_dB(m, n, i, arr_read_pivot, read_GB, G_in_adapted, A_adapted, B, K_i, K_l_adapted, arr_read_pivot_dA, G_in_dA_adapted, arr_read_pivot_dB, G_in_dB_adapted, l_range) - end - - l_range = 1:size(A)[2] - A_adapted = A[i, :] - for m in 2:cutoff_leftoverMode - for n in 2:cutoff_leftoverMode - K_l_adapted = vcat(SQRT[m], SQRT[n], K_l) - G_in_adapted = vcat(arr_read_pivot[m - 1, n, read_GB...] * SQRT[m], arr_read_pivot[m, n - 1, read_GB...] * SQRT[n], G_in[m, n, :]) - G_in_dA_adapted = vcat(reshape(arr_read_pivot_dA[m - 1, n, read_GB...,:,:], (1, size(A)...)), reshape(arr_read_pivot_dA[m, n - 1, read_GB...,:,:], (1, size(A)...)), G_in_dA[m, n,:,:,:]) - G_in_dB_adapted = vcat(reshape(arr_read_pivot_dB[m - 1, n, read_GB...,:], (1, size(B)...)), reshape(arr_read_pivot_dB[m, n - 1, read_GB...,:], (1, size(B)...)), G_in_dB[m, n,:,:]) - arr_write_dA[m, n, write...,:,:], arr_write_dB[m, n, write...,:] = calc_dA_dB(m, n, i, arr_read_pivot, read_GB, G_in_adapted, A_adapted, B, K_i, K_l_adapted, arr_read_pivot_dA, G_in_dA_adapted, arr_read_pivot_dB, G_in_dB_adapted, l_range) - end - end - return arr_write_dA, arr_write_dB -end - -function read_block(arr_write, arr_write_dA, arr_write_dB, idx_write, arr_read, arr_read_dA, arr_read_dB, idx_read_tail) - """ - Read the blocks of Fock amplitudes (of shape cutoff_leftoverMode x cutoff_leftoverMode) - and their derivatives w.r.t A and B and write them to G_in, G_in_dA, G_in_dB - """ - arr_write[:, :, idx_write] .= arr_read[:, :, idx_read_tail...] - arr_write_dA[:, :, idx_write, :, :] .= arr_read_dA[:, :, idx_read_tail...,:, :] - arr_write_dB[:, :, idx_write, :] .= arr_read_dB[:, :, idx_read_tail...,:] - return arr_write, arr_write_dA, arr_write_dB -end - - -function use_offDiag_pivot_grad!(A, B, M, cutoff_leftoverMode, cutoffs_tail, params, d, arr0, arr2, arr1010, arr1001, arr1, arr0_dA, arr2_dA, arr1010_dA, arr1001_dA, arr1_dA, arr0_dB, arr2_dB, arr1010_dB, arr1001_dB, arr1_dB, T, SQRT) - """Given params=(a,b,c,...), apply the eqs. 16 & 17 (of https://doi.org/10.22331/q-2023-08-29-1097) - for the pivots [a+1,a,b,b,c,c,...] / [a,a,b+1,b,c,c,...] / [a,a,b,b,c+1,c,...] / ...""" - pivot = CompactFock_HelperFunctions.repeat_twice(params) - pivot[2 * d - 1] += 1 - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, 2 * M) - G_in_dA = zeros(Complex{T}, size(G_in)..., size(A)...) - G_in_dB = zeros(Complex{T}, size(G_in)..., size(B)...) - - ########## READ ########## - read_GB = (2 * d - 1, params...) - GB = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, length(B)) - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - GB[m, n, :] = arr1[m, n, read_GB...] .* B - end - end - - # Array0 - G_in, G_in_dA, G_in_dB = read_block(G_in, G_in_dA, G_in_dB, 2 * d - 1, arr0, arr0_dA, arr0_dB, params) - - # read from Array2 - if params[d] > 1 - params_adapted = collect(params) - params_adapted[d] -= 1 - G_in, G_in_dA, G_in_dB = read_block(G_in, G_in_dA, G_in_dB, 2 * d, arr2, arr2_dA, arr2_dB, (d,params_adapted...)) - end - - # read from Array11 - for i in d+1:M # i>d - if params[i] > 1 - params_adapted = collect(params) - params_adapted[i] -= 1 - G_in, G_in_dA, G_in_dB = read_block(G_in, G_in_dA, G_in_dB, 2 * i - 1, arr1001, arr1001_dA, arr1001_dB, (d, i - d,params_adapted...)) - G_in, G_in_dA, G_in_dB = read_block(G_in, G_in_dA, G_in_dB, 2 * i, arr1010, arr1010_dA, arr1010_dB, (d, i - d,params_adapted...)) - end - end - - ########## WRITE ########## - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - G_in[m, n, :] .*= K_l - end - end - - # Array0 - write = collect(params) - write[d] += 1 - arr0_dA, arr0_dB = write_block_grad(2 * d + 2, write, arr1, read_GB, G_in, A, B, K_i, K_l, cutoff_leftoverMode, arr0_dA, arr1_dA, G_in_dA, arr0_dB, arr1_dB, G_in_dB, SQRT) - - # Array2 - if params[d] + 1 < cutoffs_tail[d] - write = (d, params...) - arr2_dA, arr2_dB = write_block_grad(2 * d + 1, write, arr1, read_GB, G_in, A, B, K_i, K_l, cutoff_leftoverMode, arr2_dA, arr1_dA, G_in_dA, arr2_dB, arr1_dB, G_in_dB, SQRT) - end - - # Array11 - for i in d+1:M - if params[i] < cutoffs_tail[i] - write = (d, i - d, params...) - arr1010_dA, arr1010_dB = write_block_grad(2 * i + 1, write, arr1, read_GB, G_in, A, B, K_i, K_l, cutoff_leftoverMode, arr1010_dA, arr1_dA, G_in_dA, arr1010_dB, arr1_dB, G_in_dB, SQRT) - arr1001_dA, arr1001_dB = write_block_grad(2 * i + 2, write, arr1, read_GB, G_in, A, B, K_i, K_l, cutoff_leftoverMode, arr1001_dA, arr1_dA, G_in_dA, arr1001_dB, arr1_dB, G_in_dB, SQRT) - end - end -end - -function use_diag_pivot_grad!(A, B, M, cutoff_leftoverMode, cutoffs_tail, params, arr0, arr1, arr0_dA, arr1_dA, arr0_dB, arr1_dB, T, SQRT) - pivot = CompactFock_HelperFunctions.repeat_twice(params) - """Given params=(a,b,c,...), apply the eqs. 16 & 17 (of https://doi.org/10.22331/q-2023-08-29-1097) - for the pivot [a,a,b,b,c,c...]""" - K_l = SQRT[pivot] # julia indexing counters extra zero in SQRT - K_i = SQRT[pivot .+ 1] # julia indexing counters extra zero in SQRT - G_in = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, 2*M) - G_in_dA = zeros(Complex{T}, size(G_in)..., size(A)...) - G_in_dB = zeros(Complex{T}, size(G_in)..., size(B)...) - - ########## READ ########## - read_GB = params - GB = zeros(Complex{T}, cutoff_leftoverMode, cutoff_leftoverMode, length(B)) - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - GB[m, n, :] = arr0[m, n, read_GB...] .* B - end - end - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] > 1 - params_adapted = collect(params) - params_adapted[(i-1)÷2+1] -= 1 - read = tuple(i+1-2*((i-1) % 2), params_adapted...) - G_in, G_in_dA, G_in_dB = read_block(G_in, G_in_dA, G_in_dB, i, arr1, arr1_dA, arr1_dB, read) - end - end - - ########## WRITE ########## - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - G_in[m, n, :] .*= K_l - end - end - - # Array1 - for i in 1:2*M - if params[(i-1)÷2+1] < cutoffs_tail[(i-1)÷2+1] - # this if statement prevents a few elements from being written that will never be read - if i ≠ 2 || params[1] + 1 < cutoffs_tail[1] - write = tuple(i,params...) - arr1_dA, arr1_dB = write_block_grad(i + 2, write, arr0, read_GB, G_in, A, B, K_i, K_l, cutoff_leftoverMode, arr1_dA, arr0_dA, G_in_dA, arr1_dB, arr0_dB, G_in_dB, SQRT) - end - end - end -end - -function fill_firstMode_PNRzero!(arr0,arr0_dA,arr0_dB,A,B,M,cutoff_leftoverMode,SQRT) - """fill first mode when all PNR detection values are equal to zero""" - one_tuple = tuple(fill(1,M-1)...) - - for m in 1:cutoff_leftoverMode - 1 - arr0_dA[m + 1, 1, one_tuple..., :, :] = arr0_dA[m, 1, one_tuple..., :, :] .* B[1] - arr0_dB[m + 1, 1, one_tuple..., :] = arr0_dB[m, 1, one_tuple..., :] .* B[1] - arr0_dB[m + 1, 1, one_tuple..., 1] += arr0[m, 1, one_tuple...] - if m != 1 - arr0_dA[m + 1, 1, one_tuple..., :, :] += SQRT[m] .* A[1, 1] .* arr0_dA[m - 1, 1, one_tuple..., :, :] - arr0_dA[m + 1, 1, one_tuple..., 1, 1] += SQRT[m] .* arr0[m - 1, 1, one_tuple...] - arr0_dB[m + 1, 1, one_tuple..., :] += SQRT[m] .* A[1, 1] .* arr0_dB[m - 1, 1, one_tuple..., :] - end - arr0_dA[m + 1, 1, one_tuple..., :, :] ./= SQRT[m + 1] - arr0_dB[m + 1, 1, one_tuple..., :] ./= SQRT[m + 1] - end - - for m in 1:cutoff_leftoverMode - for n in 1:cutoff_leftoverMode - 1 - arr0_dA[m, n + 1, one_tuple..., :, :] = arr0_dA[m, n, one_tuple..., :, :] .* B[2] - arr0_dB[m, n + 1, one_tuple..., :] = arr0_dB[m, n, one_tuple..., :] .* B[2] - arr0_dB[m, n + 1, one_tuple..., 2] += arr0[m, n, one_tuple...] - if m != 1 - arr0_dA[m, n + 1, one_tuple..., :, :] += SQRT[m] .* A[2, 1] .* arr0_dA[m - 1, n, one_tuple..., :, :] - arr0_dA[m, n + 1, one_tuple..., 2, 1] += SQRT[m] .* arr0[m - 1, n, one_tuple...] - arr0_dB[m, n + 1, one_tuple..., :] += SQRT[m] .* A[2, 1] .* arr0_dB[m - 1, n, one_tuple..., :] - - end - if n != 1 - arr0_dA[m, n + 1, one_tuple..., :, :] += SQRT[n] .* A[2, 2] .* arr0_dA[m, n - 1, one_tuple..., :, :] - arr0_dA[m, n + 1, one_tuple..., 2, 2] += SQRT[n] .* arr0[m, n - 1, one_tuple...] - arr0_dB[m, n + 1, one_tuple..., :] += SQRT[n] .* A[2, 2] .* arr0_dB[m, n - 1, one_tuple..., :] - end - arr0_dA[m, n + 1, one_tuple..., :, :] ./= SQRT[n + 1] - arr0_dB[m, n + 1, one_tuple..., :] ./= SQRT[n + 1] - end - end -end - -function fock_1leftoverMode_grad( - A::AbstractMatrix{Complex{Float64}}, - B::AbstractVector{Complex{Float64}}, - arr0::AbstractArray{Complex{Float64}}, - arr2::AbstractArray{Complex{Float64}}, - arr1010::AbstractArray{Complex{Float64}}, - arr1001::AbstractArray{Complex{Float64}}, - arr1::AbstractArray{Complex{Float64}}, - precision_bits::Int64 - ) - """Returns the gradients of the density matrices in the upper, undetected mode of a circuit when all other modes - are PNR detected (according to algorithm 2 of https://doi.org/10.22331/q-2023-08-29-1097) - Args: - A, B: required input for recurrence relation - Submatrices of the Fock representation. Each submatrix contains Fock indices of a certain type. - arr0 --> type: [a,a,b,b,c,c...] - arr2 --> type: [a+2,a,b,b,c,c...] / [a,a,b+2,b,c,c...] / ... - arr1010 --> type: [a+1,a,b+1,b,c,c,...] / [a+1,a,b,b,c+1,c,...] / [a,a,b+1,b,c+1,c,...] / ... - arr1001 --> type: [a+1,a,b,b+1,c,c,...] / [a+1,a,b,b,c,c+1,...] / [a,a,b+1,b,c,c+1,...] / ... - arr1 --> type: [a+1,a,b,b,c,c...] / [a,a+1,b,b,c,c...] / [a,a,b+1,b,c,c...] / ... - precision_bits: number of bits used to represent a single Fock amplitude - Returns: - arr0_dA, arr0_dB: derivatives of arr0 w.r.t A and B - """ - - T = GetPrecision.get_dtype(precision_bits) - SQRT = GetPrecision.SQRT_dict[precision_bits] - - cutoffs = size(arr0)[2:end] - M = length(cutoffs) - cutoff_leftoverMode = cutoffs[1] - cutoffs_tail = cutoffs[2:end] - - arr0_dA = zeros(Complex{T}, size(arr0)..., size(A)...) - arr2_dA = zeros(Complex{T}, size(arr2)..., size(A)...) - arr1010_dA = zeros(Complex{T}, size(arr1010)..., size(A)...) - arr1001_dA = zeros(Complex{T}, size(arr1001)..., size(A)...) - arr1_dA = zeros(Complex{T}, size(arr1)..., size(A)...) - arr0_dB = zeros(Complex{T}, size(arr0)..., size(B)...) - arr2_dB = zeros(Complex{T}, size(arr2)..., size(B)...) - arr1010_dB = zeros(Complex{T}, size(arr1010)..., size(B)...) - arr1001_dB = zeros(Complex{T}, size(arr1001)..., size(B)...) - arr1_dB = zeros(Complex{T}, size(arr1)..., size(B)...) - - fill_firstMode_PNRzero!(arr0,arr0_dA,arr0_dB,A,B,M,cutoff_leftoverMode,SQRT) - - dict_params = CompactFock_HelperFunctions.construct_dict_params(cutoffs_tail) - for sum_params in 0:sum(cutoffs_tail)-1 - for params in dict_params[sum_params] - # diagonal pivots: aa,bb,cc,... - if (cutoffs_tail[1] == 1) || (params[1] < cutoffs_tail[1]) # julia indexing! - use_diag_pivot_grad!(A,B,M - 1,cutoff_leftoverMode,cutoffs_tail,params,arr0,arr1,arr0_dA,arr1_dA,arr0_dB,arr1_dB,T,SQRT) - end - # off-diagonal pivots: d=1: (a+1)a,bb,cc,... | d=2: 00,(b+1)b,cc,... | d=3: 00,00,(c+1)c,... | ... - for d in 1:M - 1 - if all(params[1:d-1] .== 1) && (params[d] < cutoffs_tail[d]) - use_offDiag_pivot_grad!(A,B,M - 1,cutoff_leftoverMode,cutoffs_tail,params,d,arr0,arr2,arr1010,arr1001,arr1,arr0_dA,arr2_dA,arr1010_dA,arr1001_dA,arr1_dA,arr0_dB,arr2_dB,arr1010_dB,arr1001_dB,arr1_dB,T,SQRT) - end - end - end - end - - return Complex{Float64}.(arr0_dA), Complex{Float64}.(arr0_dB) -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/julia/getPrecision.jl b/mrmustard/math/lattice/strategies/julia/getPrecision.jl deleted file mode 100644 index b9e078489..000000000 --- a/mrmustard/math/lattice/strategies/julia/getPrecision.jl +++ /dev/null @@ -1,18 +0,0 @@ -module GetPrecision - -using MultiFloats - -# possible dtypes -const dtypes_dict = Dict(128 => Float64, 256 => Float64x2, 384 => Float64x3, 512 => Float64x4) - -# pre-calculate SQRT values -const SQRT_dict = Dict(k => [0;sqrt.(dtypes_dict[k].(1:999))] for k in keys(dtypes_dict)) - -function get_dtype(precision_bits) - if !(precision_bits in keys(dtypes_dict)) - error("The possible values for precision_bits are ", keys(dtypes)) - end - return dtypes_dict[precision_bits] -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/julia/vanilla.jl b/mrmustard/math/lattice/strategies/julia/vanilla.jl deleted file mode 100644 index a58907c24..000000000 --- a/mrmustard/math/lattice/strategies/julia/vanilla.jl +++ /dev/null @@ -1,82 +0,0 @@ -module Vanilla - -import ..GetPrecision - -function vanilla( - A::AbstractMatrix{Complex{Float64}}, - b::AbstractVector{Complex{Float64}}, - c::Complex{Float64}, - shape::AbstractVector{Int64}, - precision_bits::Int64 - ) - """Vanilla Fock-Bargmann strategy. Fills the tensor by iterating over all indices - in ndindex (i.e. CartesianIndices) order. - Both the input and output of this function have dtype Complex{Float64}, - but a higher precision (i.e. ``precision_bits``) is used intermediately to postpone the nummerical blowup - that results from the instable recurrence relation. - - Args: - A: A matrix of the Fock-Bargmann representation - b: B vector of the Fock-Bargmann representation - c: vacuum amplitude - shape: shape of the output tensor - precision_bits: the number of bits used to represent a single Fock amplitude - - Returns: - Array{Complex{Float64}}: Fock representation of the Gaussian tensor with shape ``shape`` - """ - - T = GetPrecision.get_dtype(precision_bits) - SQRT = GetPrecision.SQRT_dict[precision_bits] - - shape = Tuple(shape) - - path = CartesianIndices(shape) - - G = Array{Complex{T}}(undef,shape) # initialize empty array with high precision values - G[first(path)] = c - - for idx in Iterators.drop(path, 1) - update_Fock_array!(G, A, b, idx, SQRT) - end - - return Complex{Float64}.(G) # convert back to lower precision -end - -function update_Fock_array!( - G::AbstractArray, - A::AbstractMatrix{Complex{Float64}}, - b::AbstractVector{Complex{Float64}}, - idx::CartesianIndex, - SQRT::AbstractVector - ) - """Apply the recurrence relation once and update G at a certain index""" - - i, pivot = get_pivot(idx) - - @views temp = b[i] * G[pivot] - @inbounds for (j, neighbor) in get_neighbors(pivot) - @views temp += A[i, j] * G[neighbor] * SQRT[pivot[j]] - end - - G[idx] = temp / SQRT[idx[i]] -end - -function get_pivot(idx::CartesianIndex) - """returns a single idx where the first non-one value of idx has been lowered. - E.g. (1,1,5,3) -> (1,1,4,3)""" - @inbounds @simd for i in 1:length(idx) - if idx[i] > 1 - return i, CartesianIndex(ntuple(j -> j == i ? idx[j] - 1 : idx[j], length(idx))) - end - end - return 1, idx -end - -function get_neighbors(idx::CartesianIndex{N}) where {N} - """Does the same as get_neighbors, but skips over the first "start" iterations""" - return ((i, CartesianIndex(ntuple(d -> d == i ? idx[d] - 1 : idx[d], Val(N)))) - for i in 1:N if idx[i] > 1) -end - -end # end module diff --git a/mrmustard/math/lattice/strategies/vanilla.jl b/mrmustard/math/lattice/strategies/vanilla.jl deleted file mode 100644 index a502f2706..000000000 --- a/mrmustard/math/lattice/strategies/vanilla.jl +++ /dev/null @@ -1,73 +0,0 @@ -using MultiFloats -T = Float64x4 -SQRT = [0.0; [sqrt(T(i)) for i in 1:100000]] - -function vanilla( - A::AbstractMatrix{Complex{Float64}}, - b::AbstractVector{Complex{Float64}}, - c::Complex{Float64}, - shape::AbstractVector{Int64} - ) - """Vanilla Fock-Bargmann strategy. Fills the tensor by iterating over all indices - in ndindex (i.e. CartesianIndices) order. - Both the input and output of this function have dtype Complex{Float64}, - but Complex{Float64x4} is used intermediately to postpone the nummerical blowup - that results from the instable recurrence relation. - - Args: - A: A matrix of the Fock-Bargmann representation - b: B vector of the Fock-Bargmann representation - c: vacuum amplitude - shape: shape of the output tensor - - Returns: - Array{Complex{Float64}}: Fock representation of the Gaussian tensor with shape ``shape`` - """ - - shape = Tuple(shape) - - path = CartesianIndices(shape) - - G = Array{Complex{T}}(undef,shape) # initialize empty array with high precision values - G[first(path)] = c - - for idx in Iterators.drop(path, 1) - update_Fock_array!(G, A, b, idx) - end - - return Complex{Float64}.(G) # convert back to lower precision -end - -function update_Fock_array!( - G::AbstractArray{Complex{T}}, - A::AbstractMatrix{Complex{Float64}}, - b::AbstractVector{Complex{Float64}}, - idx::CartesianIndex, - ) - - i, pivot = get_pivot(idx) - - @views temp = b[i] * G[pivot] - @inbounds for (j, neighbor) in get_neighbors(pivot) - @views temp += A[i, j] * G[neighbor] * SQRT[pivot[j]] - end - - G[idx] = temp / SQRT[idx[i]] -end - -function get_pivot(idx::CartesianIndex) - """returns a single idx where the first non-one value of idx has been lowered. - E.g. (1,1,5,3) -> (1,1,4,3)""" - @inbounds @simd for i in 1:length(idx) - if idx[i] > 1 - return i, CartesianIndex(ntuple(j -> j == i ? idx[j] - 1 : idx[j], length(idx))) - end - end - return 1, idx -end - -function get_neighbors(idx::CartesianIndex{N}) where {N} - """Does the same as get_neighbors, but skips over the first "start" iterations""" - return ((i, CartesianIndex(ntuple(d -> d == i ? idx[d] - 1 : idx[d], Val(N)))) - for i in 1:N if idx[i] > 1) -end diff --git a/mrmustard/utils/settings.py b/mrmustard/utils/settings.py index 49a28706d..eed8e33c7 100644 --- a/mrmustard/utils/settings.py +++ b/mrmustard/utils/settings.py @@ -60,7 +60,6 @@ def __init__(self): self.rng = np.random.default_rng(self._seed) self._precision_bits_hermite_poly: int = 128 self._complex_warning: bool = False - self._julia_initialized: bool = False self._cache_dir = Path(__file__).parents[2].absolute() / ".serialize_cache" self.AUTOSHAPE_PROBABILITY: float = 0.99999 @@ -155,46 +154,6 @@ def HBAR(self, value: float): warnings.warn("Changing HBAR can conflict with prior computations.") self._hbar = value - @property - def PRECISION_BITS_HERMITE_POLY(self): - r""" - The number of bits used to represent a single Fock amplitude when calculating Hermite polynomials. - Default is 128 (i.e. the Fock representation has dtype complex128). - Currently allowed values: 128, 256, 384, 512 - """ - return self._precision_bits_hermite_poly - - @PRECISION_BITS_HERMITE_POLY.setter - def PRECISION_BITS_HERMITE_POLY(self, value: int): - allowed_values = [128, 256, 384, 512] - if value not in allowed_values: - raise ValueError( - f"precision_bits_hermite_poly must be one of the following values: {allowed_values}" - ) - self._precision_bits_hermite_poly = value - if ( - value != 128 and not self._julia_initialized - ): # initialize Julia when precision > complex128 and if it wasn't initialized before - from juliacall import Main as jl # pylint: disable=import-outside-toplevel - - # import Julia functions - julia_directory = ( - Path(__file__) - .parent.parent.joinpath("math", "lattice", "strategies", "julia") - .resolve() - .absolute() - ) - jl.cd(str(julia_directory)) - jl.include("getPrecision.jl") - jl.include("vanilla.jl") - jl.include("compactFock/helperFunctions.jl") - jl.include("compactFock/diagonal_amps.jl") - jl.include("compactFock/diagonal_grad.jl") - jl.include("compactFock/singleLeftoverMode_amps.jl") - jl.include("compactFock/singleLeftoverMode_grad.jl") - - self._julia_initialized = True - @property def SEED(self) -> int: r"""Returns the seed value if set, otherwise returns a random seed.""" diff --git a/poetry.lock b/poetry.lock index f0a4d9627..c9cae71fc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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b/tests/test_math/test_compactFock.py index f44391911..5f2aa7f26 100644 --- a/tests/test_math/test_compactFock.py +++ b/tests/test_math/test_compactFock.py @@ -18,9 +18,6 @@ from ..conftest import skip_np -do_julia = bool(importlib.util.find_spec("juliacall")) -precisions = [128, 256, 384, 512] if do_julia else [128] - @st.composite def random_ABC(draw, M): @@ -34,118 +31,108 @@ def random_ABC(draw, M): @given(random_ABC(M=3)) -@pytest.mark.parametrize("precision", precisions) -def test_compactFock_diagonal(precision, A_B_G0): +def test_compactFock_diagonal(A_B_G0): r"""Test getting Fock amplitudes if all modes are detected (math.hermite_renormalized_diagonal) """ - with settings(PRECISION_BITS_HERMITE_POLY=precision): - cutoffs = (5, 5, 5) - - A, B, G0 = A_B_G0 # Create random state (M mode Gaussian state with displacement) - - # Vanilla MM - G_ref = math.hermite_renormalized( - math.conj(-A), math.conj(B), math.conj(G0), shape=list(cutoffs) * 2 - ) # note: shape=[C1,C2,C3,...,C1,C2,C3,...] - G_ref = math.asnumpy(G_ref) - - # Extract diagonal amplitudes from vanilla MM - ref_diag = np.zeros(cutoffs, dtype=np.complex128) - for inds in np.ndindex(*cutoffs): - inds_expanded = list(inds) + list(inds) # a,b,c,a,b,c - ref_diag[inds] = G_ref[tuple(inds_expanded)] - - # New MM - G_diag = math.hermite_renormalized_diagonal( - math.conj(-A), math.conj(B), math.conj(G0), cutoffs - ) - assert np.allclose(ref_diag, G_diag) + cutoffs = (5, 5, 5) + + A, B, G0 = A_B_G0 # Create random state (M mode Gaussian state with displacement) + + # Vanilla MM + G_ref = math.hermite_renormalized( + math.conj(-A), math.conj(B), math.conj(G0), shape=list(cutoffs) * 2 + ) # note: shape=[C1,C2,C3,...,C1,C2,C3,...] + G_ref = math.asnumpy(G_ref) + + # Extract diagonal amplitudes from vanilla MM + ref_diag = np.zeros(cutoffs, dtype=np.complex128) + for inds in np.ndindex(*cutoffs): + inds_expanded = list(inds) + list(inds) # a,b,c,a,b,c + ref_diag[inds] = G_ref[tuple(inds_expanded)] + + # New MM + G_diag = math.hermite_renormalized_diagonal(math.conj(-A), math.conj(B), math.conj(G0), cutoffs) + assert np.allclose(ref_diag, G_diag) @given(random_ABC(M=3)) -@pytest.mark.parametrize("precision", precisions) -def test_compactFock_1leftover(precision, A_B_G0): +def test_compactFock_1leftover(A_B_G0): r""" Test getting Fock amplitudes if all but the first mode are detected (math.hermite_renormalized_1leftoverMode). """ skip_np() - with settings(PRECISION_BITS_HERMITE_POLY=precision): - cutoffs = (5, 5, 5) + cutoffs = (5, 5, 5) - A, B, G0 = A_B_G0 # Create random state (M mode Gaussian state with displacement) + A, B, G0 = A_B_G0 # Create random state (M mode Gaussian state with displacement) - # New algorithm - G_leftover = math.hermite_renormalized_1leftoverMode( - math.conj(-A), math.conj(B), math.conj(G0), cutoffs - ) + # New algorithm + G_leftover = math.hermite_renormalized_1leftoverMode( + math.conj(-A), math.conj(B), math.conj(G0), cutoffs + ) - # Vanilla MM - G_ref = math.hermite_renormalized( - math.conj(-A), math.conj(B), math.conj(G0), shape=list(cutoffs) * 2 - ) # note: shape=[C1,C2,C3,...,C1,C2,C3,...] - G_ref = math.asnumpy(G_ref) + # Vanilla MM + G_ref = math.hermite_renormalized( + math.conj(-A), math.conj(B), math.conj(G0), shape=list(cutoffs) * 2 + ) # note: shape=[C1,C2,C3,...,C1,C2,C3,...] + G_ref = math.asnumpy(G_ref) - # Extract amplitudes of leftover mode from vanilla MM - ref_leftover = np.zeros([cutoffs[0]] * 2 + list(cutoffs)[1:], dtype=np.complex128) - for inds in np.ndindex(*cutoffs[1:]): - ref_leftover[tuple([slice(cutoffs[0]), slice(cutoffs[0])] + list(inds))] = G_ref[ - tuple([slice(cutoffs[0])] + list(inds) + [slice(cutoffs[0])] + list(inds)) - ] - assert np.allclose(ref_leftover, G_leftover) + # Extract amplitudes of leftover mode from vanilla MM + ref_leftover = np.zeros([cutoffs[0]] * 2 + list(cutoffs)[1:], dtype=np.complex128) + for inds in np.ndindex(*cutoffs[1:]): + ref_leftover[tuple([slice(cutoffs[0]), slice(cutoffs[0])] + list(inds))] = G_ref[ + tuple([slice(cutoffs[0])] + list(inds) + [slice(cutoffs[0])] + list(inds)) + ] + assert np.allclose(ref_leftover, G_leftover) -@pytest.mark.parametrize("precision", precisions) -def test_compactFock_diagonal_gradients(precision): +def test_compactFock_diagonal_gradients(): r""" Test getting Fock amplitudes and gradients if all modes are detected (math.hermite_renormalized_diagonal). """ skip_np() - with settings(PRECISION_BITS_HERMITE_POLY=precision): - G = Ggate(num_modes=1, symplectic_trainable=True) + G = Ggate(num_modes=1, symplectic_trainable=True) - def cost_fn(): - n1 = 2 # number of detected photons - state_opt = Vacuum(1) >> G - A, B, G0 = wigner_to_bargmann_rho(state_opt.cov, state_opt.means) - probs = math.hermite_renormalized_diagonal( - math.conj(-A), math.conj(B), math.conj(G0), cutoffs=[n1 + 1] - ) - p = probs[n1] - return -math.real(p) + def cost_fn(): + n1 = 2 # number of detected photons + state_opt = Vacuum(1) >> G + A, B, G0 = wigner_to_bargmann_rho(state_opt.cov, state_opt.means) + probs = math.hermite_renormalized_diagonal( + math.conj(-A), math.conj(B), math.conj(G0), cutoffs=[n1 + 1] + ) + p = probs[n1] + return -math.real(p) - opt = Optimizer(symplectic_lr=0.5) - opt.minimize(cost_fn, by_optimizing=[G], max_steps=5) - for i in range(2, min(20, len(opt.opt_history))): - assert opt.opt_history[i - 1] >= opt.opt_history[i] + opt = Optimizer(symplectic_lr=0.5) + opt.minimize(cost_fn, by_optimizing=[G], max_steps=5) + for i in range(2, min(20, len(opt.opt_history))): + assert opt.opt_history[i - 1] >= opt.opt_history[i] -@pytest.mark.parametrize("precision", precisions) -def test_compactFock_1leftover_gradients(precision): +def test_compactFock_1leftover_gradients(): r""" Test getting Fock amplitudes and if all but the first mode are detected (math.hermite_renormalized_1leftoverMode). """ skip_np() - with settings(PRECISION_BITS_HERMITE_POLY=precision): - G = Ggate(num_modes=2, symplectic_trainable=True) - - def cost_fn(): - n2 = 2 # number of detected photons - state_opt = Vacuum(2) >> G - A, B, G0 = wigner_to_bargmann_rho(state_opt.cov, state_opt.means) - marginal = math.hermite_renormalized_1leftoverMode( - math.conj(-A), math.conj(B), math.conj(G0), cutoffs=[2, n2 + 1] - ) - conditional_state = normalize(State(dm=marginal[..., n2])) - return -fidelity(conditional_state, SqueezedVacuum(r=1)) - - opt = Optimizer(symplectic_lr=0.1) - opt.minimize(cost_fn, by_optimizing=[G], max_steps=5) - for i in range(2, len(opt.opt_history)): - assert opt.opt_history[i - 1] >= opt.opt_history[i] + G = Ggate(num_modes=2, symplectic_trainable=True) + + def cost_fn(): + n2 = 2 # number of detected photons + state_opt = Vacuum(2) >> G + A, B, G0 = wigner_to_bargmann_rho(state_opt.cov, state_opt.means) + marginal = math.hermite_renormalized_1leftoverMode( + math.conj(-A), math.conj(B), math.conj(G0), cutoffs=[2, n2 + 1] + ) + conditional_state = normalize(State(dm=marginal[..., n2])) + return -fidelity(conditional_state, SqueezedVacuum(r=1)) + + opt = Optimizer(symplectic_lr=0.1) + opt.minimize(cost_fn, by_optimizing=[G], max_steps=5) + for i in range(2, len(opt.opt_history)): + assert opt.opt_history[i - 1] >= opt.opt_history[i] diff --git a/tests/test_math/test_lattice.py b/tests/test_math/test_lattice.py index c441b6283..835993da4 100644 --- a/tests/test_math/test_lattice.py +++ b/tests/test_math/test_lattice.py @@ -33,24 +33,10 @@ from mrmustard.math.lattice.strategies.vanilla import vanilla_stable, vanilla_stable_batch from mrmustard.physics.bargmann_utils import wigner_to_bargmann_rho -original_precision = settings.PRECISION_BITS_HERMITE_POLY - -do_julia = bool(importlib.util.find_spec("juliacall")) -precisions = ( - [128, 256, 384, 512] - if do_julia - else [ - 128, - ] -) - -@pytest.mark.parametrize("precision", precisions) -def test_vanillaNumba_vs_binomial(precision): - """Test that the vanilla method and the binomial method give the same result. - Test is repeated for all possible values of PRECISION_BITS_HERMITE_POLY.""" +def test_vanillaNumba_vs_binomial(): + """Test that the vanilla method and the binomial method give the same result.""" - settings.PRECISION_BITS_HERMITE_POLY = precision G = Gaussian(2) ket_vanilla = G.ket(cutoffs=[10, 10])[:5, :5] @@ -58,8 +44,6 @@ def test_vanillaNumba_vs_binomial(precision): assert np.allclose(ket_vanilla, ket_binomial) - settings.PRECISION_BITS_HERMITE_POLY = original_precision - def test_binomial_vs_binomialDict(): """Test that binomial and binomial_dict give the same result.""" diff --git a/tests/test_utils/test_settings.py b/tests/test_utils/test_settings.py index 08fa18c7d..bfafd9e23 100644 --- a/tests/test_utils/test_settings.py +++ b/tests/test_utils/test_settings.py @@ -43,7 +43,6 @@ def test_init(self): assert settings.EQ_TRANSFORMATION_RTOL_GAUSS == 1e-6 assert settings.PNR_INTERNAL_CUTOFF == 50 assert settings.HOMODYNE_SQUEEZING == 10.0 - assert settings.PRECISION_BITS_HERMITE_POLY == 128 assert settings.PROGRESSBAR is True assert settings.BS_FOCK_METHOD == "vanilla" @@ -60,18 +59,10 @@ def test_setters(self): assert settings.SEED is not None settings.SEED = s0 - p0 = settings.PRECISION_BITS_HERMITE_POLY - settings.PRECISION_BITS_HERMITE_POLY = 256 - assert settings.PRECISION_BITS_HERMITE_POLY == 256 - settings.PRECISION_BITS_HERMITE_POLY = p0 - assert settings.HBAR == 1.0 with pytest.warns(UserWarning, match="Changing HBAR can conflict with prior computations"): settings.HBAR = 3 - with pytest.raises(ValueError, match="precision_bits_hermite_poly"): - settings.PRECISION_BITS_HERMITE_POLY = 9 - def test_settings_seed_randomness_at_init(self): """Test that the random seed is set randomly as MM is initialized.""" settings = Settings() From 41a94ad7de50da4f7aa684a05ae3b2128ef93fdf Mon Sep 17 00:00:00 2001 From: Anthony Date: Mon, 20 Jan 2025 09:45:13 -0500 Subject: [PATCH 2/7] codefactor --- tests/test_math/test_compactFock.py | 5 +---- tests/test_math/test_lattice.py | 2 -- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/tests/test_math/test_compactFock.py b/tests/test_math/test_compactFock.py index 5f2aa7f26..a31a5b4d5 100644 --- a/tests/test_math/test_compactFock.py +++ b/tests/test_math/test_compactFock.py @@ -2,14 +2,11 @@ Unit tests for mrmustard.math.compactFock.compactFock~ """ -import importlib - import numpy as np -import pytest from hypothesis import given from hypothesis import strategies as st -from mrmustard import math, settings +from mrmustard import math from mrmustard.lab import Ggate, SqueezedVacuum, State, Vacuum from mrmustard.physics import fidelity, normalize from mrmustard.physics.bargmann_utils import wigner_to_bargmann_rho diff --git a/tests/test_math/test_lattice.py b/tests/test_math/test_lattice.py index 835993da4..8d4bdf0be 100644 --- a/tests/test_math/test_lattice.py +++ b/tests/test_math/test_lattice.py @@ -14,8 +14,6 @@ """Tests for the lattice module""" -import importlib - import numpy as np import pytest From afb4d9cbf5dbdbc6ee99f937cafe43ef6300eff0 Mon Sep 17 00:00:00 2001 From: Anthony Date: Mon, 20 Jan 2025 10:59:04 -0500 Subject: [PATCH 3/7] update lock file --- poetry.lock | 1 - 1 file changed, 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index c9cae71fc..5c5470576 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1528,7 +1528,6 @@ description = "Clang Python Bindings, mirrored from the official LLVM repo: http optional = false python-versions = "*" files = [ - {file = "libclang-18.1.1-1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:0b2e143f0fac830156feb56f9231ff8338c20aecfe72b4ffe96f19e5a1dbb69a"}, {file = "libclang-18.1.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:6f14c3f194704e5d09769108f03185fce7acaf1d1ae4bbb2f30a72c2400cb7c5"}, {file = "libclang-18.1.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:83ce5045d101b669ac38e6da8e58765f12da2d3aafb3b9b98d88b286a60964d8"}, {file = "libclang-18.1.1-py2.py3-none-manylinux2010_x86_64.whl", hash = "sha256:c533091d8a3bbf7460a00cb6c1a71da93bffe148f172c7d03b1c31fbf8aa2a0b"}, From cf579c85e2279c1327f2f06396c9db5394a995e5 Mon Sep 17 00:00:00 2001 From: Anthony Date: Mon, 20 Jan 2025 11:01:36 -0500 Subject: [PATCH 4/7] poetry --- poetry.lock | 1587 +++++++++++++++++++++++++-------------------------- 1 file changed, 791 insertions(+), 796 deletions(-) diff --git a/poetry.lock b/poetry.lock index 5c5470576..aa4ce9eef 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. 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-10,8 +10,6 @@ build: - graphviz jobs: post_install: - - "curl -fsSL https://install.julialang.org | sh -s -- --yes" - - "ln -s /home/docs/.juliaup/bin/julia /home/docs/.asdf/bin/julia" - pip install --no-cache-dir poetry==1.4.0 - poetry config virtualenvs.create false - poetry install --with doc From 5d28d897651330a01ba8f58e604e8c63a04b38c2 Mon Sep 17 00:00:00 2001 From: Austin Lund Date: Tue, 21 Jan 2025 09:21:31 +1000 Subject: [PATCH 6/7] Update poetry lock file --- poetry.lock | 1 + 1 file changed, 1 insertion(+) diff --git a/poetry.lock b/poetry.lock index 11839f88e..ce683bb1b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1544,6 +1544,7 @@ description = "Clang Python Bindings, mirrored from the official LLVM repo: http optional = false python-versions = "*" files = [ + {file = "libclang-18.1.1-1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:0b2e143f0fac830156feb56f9231ff8338c20aecfe72b4ffe96f19e5a1dbb69a"}, {file = "libclang-18.1.1-py2.py3-none-macosx_10_9_x86_64.whl", hash 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Python. -Increased numerical stability using Julia [optional] ------------------- -Converting phase space objects to Fock space can be numerically unstable due to accumulating floating point errors. -To resolve this, the conversion can be performed with extended-precision arithmetic. 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