Merge remote-tracking branch 'origin/master' into elementwise-func-fixes

Author: oleksandr-pavlyk

Diff for: .github/workflows/generate-coverage.yaml

@@ -79,7 +79,7 @@ jobs:
       - name: Install dpctl dependencies
         shell: bash -l {0}
         run: |
-          pip install numpy cython setuptools pytest pytest-cov scikit-build cmake coverage[toml]
+          pip install numpy cython"<3" setuptools pytest pytest-cov scikit-build cmake coverage[toml]
 
       - name: Build dpctl with coverage
         shell: bash -l {0}

Diff for: .github/workflows/generate-docs.yml

@@ -26,7 +26,7 @@ jobs:
       - name: Install Intel OneAPI
         if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
         run: |
-          sudo apt-get install intel-oneapi-dpcpp-cpp-compiler
+          sudo apt-get install intel-oneapi-compiler-dpcpp-cpp
       - name: Install Lua
         if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
         run: |
@@ -49,7 +49,7 @@ jobs:
         if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
         shell: bash -l {0}
         run: |
-          pip install numpy cython setuptools scikit-build cmake sphinx sphinx_rtd_theme pydot graphviz sphinxcontrib-programoutput sphinxcontrib-googleanalytics
+          pip install numpy cython"<3" setuptools scikit-build cmake sphinx sphinx_rtd_theme pydot graphviz sphinxcontrib-programoutput sphinxcontrib-googleanalytics
       - name: Checkout repo
         uses: actions/checkout@v3
         with:

Diff for: .github/workflows/os-llvm-sycl-build.yml

@@ -11,9 +11,9 @@ jobs:
 
     env:
       DOWNLOAD_URL_PREFIX: https://github.com/intel/llvm/releases/download
-      DRIVER_PATH: 2023-WW13
-      OCLCPUEXP_FN: oclcpuexp-2023.15.3.0.20_rel.tar.gz
-      FPGAEMU_FN: fpgaemu-2023.15.3.0.20_rel.tar.gz
+      DRIVER_PATH: 2023-WW27
+      OCLCPUEXP_FN: oclcpuexp-2023.16.6.0.28_rel.tar.gz
+      FPGAEMU_FN: fpgaemu-2023.16.6.0.28_rel.tar.gz
       TBB_URL: https://github.com/oneapi-src/oneTBB/releases/download/v2021.9.0/
       TBB_INSTALL_DIR: oneapi-tbb-2021.9.0
       TBB_FN: oneapi-tbb-2021.9.0-lin.tgz
@@ -37,34 +37,42 @@ jobs:
             ${{ runner.os }}-
 
       - name: Download and install nightly and components
+        env:
+          USE_LATEST_SYCLOS: 0
         shell: bash -l {0}
         run: |
           cd /home/runner/work
           mkdir -p sycl_bundle
           cd sycl_bundle
-          # get list of shas and tags from remote, filter sycl-nightly tags and reverse order
-          export LLVM_TAGS=$(git -c 'versionsort.suffix=-' ls-remote --tags --sort='v:refname' https://github.com/intel/llvm.git | \
-                       grep sycl-nightly | awk '{a[i++]=$0} END {for (j=i-1; j>=0;) print a[j--] }')
-          # initialize
-          unset DEPLOY_NIGHTLY_TAG
-          unset DEPLOY_NIGHTLY_TAG_SHA
-
-          # go through tags and find the most recent one where nighly build binary is available
-          while IFS= read -r NEXT_LLVM_TAG; do
-              export NEXT_LLVM_TAG_SHA=$(echo ${NEXT_LLVM_TAG} | awk '{print $1}')
-              export NEXT_NIGHTLY_TAG=$(python3 -c "import sys, urllib.parse as ul; print (ul.quote_plus(sys.argv[1]))" \
-                                          $(echo ${NEXT_LLVM_TAG} | awk '{gsub(/^refs\/tags\//, "", $2)} {print $2}'))
-              if [[ `wget -S --spider ${DOWNLOAD_URL_PREFIX}/${NEXT_NIGHTLY_TAG}/dpcpp-compiler.tar.gz  2>&1 | grep 'HTTP/1.1 200 OK'` ]];
-              then
-                  export DEPLOY_NIGHTLY_TAG=${NEXT_NIGHTLY_TAG}
-                  export DEPLOY_LLVM_TAG_SHA=${NEXT_LLVM_TAG_SHA}
-                  break
-              fi
-          done <<< "${LLVM_TAGS}"
+          if [[ "${USE_LATEST_SYCLOS:-0}" -eq "1" ]]; then
+              # get list of shas and tags from remote, filter sycl-nightly tags and reverse order
+              export LLVM_TAGS=$(git -c 'versionsort.suffix=-' ls-remote --tags --sort='v:refname' https://github.com/intel/llvm.git | \
+                          grep sycl-nightly | awk '{a[i++]=$0} END {for (j=i-1; j>=0;) print a[j--] }')
+              # initialize
+              unset DEPLOY_NIGHTLY_TAG
+              unset DEPLOY_NIGHTLY_TAG_SHA
+
+              # go through tags and find the most recent one where nighly build binary is available
+              while IFS= read -r NEXT_LLVM_TAG; do
+                  export NEXT_LLVM_TAG_SHA=$(echo ${NEXT_LLVM_TAG} | awk '{print $1}')
+                  export NEXT_NIGHTLY_TAG=$(python3 -c "import sys, urllib.parse as ul; print (ul.quote_plus(sys.argv[1]))" \
+                                              $(echo ${NEXT_LLVM_TAG} | awk '{gsub(/^refs\/tags\//, "", $2)} {print $2}'))
+                  if [[ `wget -S --spider ${DOWNLOAD_URL_PREFIX}/${NEXT_NIGHTLY_TAG}/dpcpp-compiler.tar.gz  2>&1 | grep 'HTTP/1.1 200 OK'` ]];
+                  then
+                      export DEPLOY_NIGHTLY_TAG=${NEXT_NIGHTLY_TAG}
+                      export DEPLOY_LLVM_TAG_SHA=${NEXT_LLVM_TAG_SHA}
+                      break
+                  fi
+              done <<< "${LLVM_TAGS}"
+          else
+              # Use latest known to work tag instead
+              export DEPLOY_NIGHTLY_TAG="sycl-nightly%2F20230606"
+              export DEPLOY_LLVM_TAG_SHA=f44d0133d4b0077298f034697a1f3818ff1d6134
+          fi
 
           [[ -n "${DEPLOY_NIGHTLY_TAG}" ]] || exit 1
           [[ -n "${DEPLOY_LLVM_TAG_SHA}" ]] || exit 1
-          echo "Using ${m} corresponding to intel/llvm at ${DEPLOY_LLVM_TAG_SHA}"
+          echo "Using ${DEPLOY_NIGHTLY_TAG} corresponding to intel/llvm at ${DEPLOY_LLVM_TAG_SHA}"
 
           if [[ -f bundle_id.txt && ( "$(cat bundle_id.txt)" == "${DEPLOY_LLVM_TAG_SHA}" ) ]]; then
               echo "Using cached download of ${DEPLOY_LLVM_TAG_SHA}"
@@ -100,7 +108,7 @@ jobs:
       - name: Install dpctl dependencies
         shell: bash -l {0}
         run: |
-          pip install numpy cython setuptools pytest scikit-build cmake
+          pip install numpy cython"<3" setuptools pytest scikit-build cmake
 
       - name: Checkout repo
         uses: actions/checkout@v3

Diff for: conda-recipe/meta.yaml

@@ -20,7 +20,7 @@ requirements:
         - cmake  >=3.21
         - ninja
         - git
-        - cython
+        - cython  <3
         - python
         - scikit-build
         - numpy

Diff for: dpctl/tensor/__init__.py

@@ -94,12 +94,14 @@
 from ._elementwise_funcs import (
     abs,
     add,
+    ceil,
     conj,
     cos,
     divide,
     equal,
     exp,
     expm1,
+    floor,
     floor_divide,
     greater,
     greater_equal,
@@ -128,6 +130,7 @@
     sqrt,
     square,
     subtract,
+    trunc,
 )
 from ._reduction import sum
 
@@ -208,16 +211,21 @@
     "inf",
     "abs",
     "add",
+    "ceil",
     "conj",
     "cos",
+    "divide",
+    "equal",
     "exp",
     "expm1",
+    "floor",
+    "floor_divide",
     "greater",
     "greater_equal",
     "imag",
+    "isfinite",
     "isinf",
     "isnan",
-    "isfinite",
     "less",
     "less_equal",
     "log",
@@ -228,19 +236,17 @@
     "log1p",
     "log2",
     "log10",
+    "multiply",
     "negative",
+    "not_equal",
     "positive",
+    "pow",
     "proj",
     "real",
     "sin",
     "sqrt",
     "square",
-    "divide",
-    "multiply",
-    "pow",
     "subtract",
-    "equal",
-    "not_equal",
     "sum",
-    "floor_divide",
+    "trunc",
 ]

Diff for: dpctl/tensor/_copy_utils.py

@@ -213,6 +213,11 @@ def _copy_same_shape(dst, src):
     """Assumes src and dst have the same shape."""
     # check that memory regions do not overlap
     if ti._array_overlap(dst, src):
+        if src._pointer == dst._pointer and (
+            src is dst
+            or (src.strides == dst.strides and src.dtype == dst.dtype)
+        ):
+            return
         _copy_overlapping(src=src, dst=dst)
         return
 

Diff for: dpctl/tensor/_elementwise_common.py

@@ -52,6 +52,20 @@ def __call__(self, x, out=None, order="K"):
         if not isinstance(x, dpt.usm_ndarray):
             raise TypeError(f"Expected dpctl.tensor.usm_ndarray, got {type(x)}")
 
+        if order not in ["C", "F", "K", "A"]:
+            order = "K"
+        buf_dt, res_dt = _find_buf_dtype(
+            x.dtype, self.result_type_resolver_fn_, x.sycl_device
+        )
+        if res_dt is None:
+            raise TypeError(
+                f"function '{self.name_}' does not support input type "
+                f"({x.dtype}), "
+                "and the input could not be safely coerced to any "
+                "supported types according to the casting rule ''safe''."
+            )
+
+        orig_out = out
         if out is not None:
             if not isinstance(out, dpt.usm_ndarray):
                 raise TypeError(
@@ -64,8 +78,21 @@ def __call__(self, x, out=None, order="K"):
                     f"Expected output shape is {x.shape}, got {out.shape}"
                 )
 
-            if ti._array_overlap(x, out):
-                raise TypeError("Input and output arrays have memory overlap")
+            if res_dt != out.dtype:
+                raise TypeError(
+                    f"Output array of type {res_dt} is needed,"
+                    f" got {out.dtype}"
+                )
+
+            if (
+                buf_dt is None
+                and ti._array_overlap(x, out)
+                and not ti._same_logical_tensors(x, out)
+            ):
+                # Allocate a temporary buffer to avoid memory overlapping.
+                # Note if `buf_dt` is not None, a temporary copy of `x` will be
+                # created, so the array overlap check isn't needed.
+                out = dpt.empty_like(out)
 
             if (
                 dpctl.utils.get_execution_queue((x.sycl_queue, out.sycl_queue))
@@ -75,13 +102,6 @@ def __call__(self, x, out=None, order="K"):
                     "Input and output allocation queues are not compatible"
                 )
 
-        if order not in ["C", "F", "K", "A"]:
-            order = "K"
-        buf_dt, res_dt = _find_buf_dtype(
-            x.dtype, self.result_type_resolver_fn_, x.sycl_device
-        )
-        if res_dt is None:
-            raise RuntimeError
         exec_q = x.sycl_queue
         if buf_dt is None:
             if out is None:
@@ -91,17 +111,20 @@ def __call__(self, x, out=None, order="K"):
                     if order == "A":
                         order = "F" if x.flags.f_contiguous else "C"
                     out = dpt.empty_like(x, dtype=res_dt, order=order)
-            else:
-                if res_dt != out.dtype:
-                    raise TypeError(
-                        f"Output array of type {res_dt} is needed,"
-                        f" got {out.dtype}"
-                    )
 
-            ht, _ = self.unary_fn_(x, out, sycl_queue=exec_q)
-            ht.wait()
+            ht_unary_ev, unary_ev = self.unary_fn_(x, out, sycl_queue=exec_q)
+
+            if not (orig_out is None or orig_out is out):
+                # Copy the out data from temporary buffer to original memory
+                ht_copy_ev, _ = ti._copy_usm_ndarray_into_usm_ndarray(
+                    src=out, dst=orig_out, sycl_queue=exec_q, depends=[unary_ev]
+                )
+                ht_copy_ev.wait()
+                out = orig_out
 
+            ht_unary_ev.wait()
             return out
+
         if order == "K":
             buf = _empty_like_orderK(x, buf_dt)
         else:
@@ -117,11 +140,6 @@ def __call__(self, x, out=None, order="K"):
                 out = _empty_like_orderK(buf, res_dt)
             else:
                 out = dpt.empty_like(buf, dtype=res_dt, order=order)
-        else:
-            if buf_dt != out.dtype:
-                raise TypeError(
-                    f"Output array of type {buf_dt} is needed, got {out.dtype}"
-                )
 
         ht, _ = self.unary_fn_(buf, out, sycl_queue=exec_q, depends=[copy_ev])
         ht_copy_ev.wait()

Diff for: dpctl/tensor/_elementwise_funcs.py

@@ -114,7 +114,30 @@
 # FIXME: implement B07
 
 # U09: ==== CEIL          (x)
-# FIXME: implement U09
+_ceil_docstring = """
+ceil(x, out=None, order='K')
+
+Returns the ceiling for each element `x_i` for input array `x`.
+The ceil of the scalar `x` is the smallest integer `i`, such that `i >= x`.
+
+Args:
+    x (usm_ndarray):
+        Input array, expected to have numeric data type.
+    out ({None, usm_ndarray}, optional):
+        Output array to populate.
+        Array have the correct shape and the expected data type.
+    order ("C","F","A","K", optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    usm_narray:
+        An array containing the element-wise ceiling of input array.
+        The returned array has the same data type as `x`.
+"""
+
+ceil = UnaryElementwiseFunc(
+    "ceil", ti._ceil_result_type, ti._ceil, _ceil_docstring
+)
 
 # U10: ==== CONJ          (x)
 _conj_docstring = """
@@ -271,7 +294,30 @@
 )
 
 # U15: ==== FLOOR         (x)
-# FIXME: implement U15
+_floor_docstring = """
+floor(x, out=None, order='K')
+
+Returns the floor for each element `x_i` for input array `x`.
+The floor of the scalar `x` is the largest integer `i`, such that `i <= x`.
+
+Args:
+    x (usm_ndarray):
+        Input array, expected to have numeric data type.
+    out ({None, usm_ndarray}, optional):
+        Output array to populate.
+        Array have the correct shape and the expected data type.
+    order ("C","F","A","K", optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    usm_narray:
+        An array containing the element-wise floor of input array.
+        The returned array has the same data type as `x`.
+"""
+
+floor = UnaryElementwiseFunc(
+    "floor", ti._floor_result_type, ti._floor, _floor_docstring
+)
 
 # B10: ==== FLOOR_DIVIDE  (x1, x2)
 _floor_divide_docstring_ = """
@@ -1031,4 +1077,29 @@
 # FIXME: implement U35
 
 # U36: ==== TRUNC       (x)
-# FIXME: implement U36
+_trunc_docstring = """
+trunc(x, out=None, order='K')
+
+Returns the truncated value for each element `x_i` for input array `x`.
+The truncated value of the scalar `x` is the nearest integer i which is
+ closer to zero than `x` is. In short, the fractional part of the
+ signed number `x` is discarded.
+
+Args:
+    x (usm_ndarray):
+        Input array, expected to have numeric data type.
+    out ({None, usm_ndarray}, optional):
+        Output array to populate.
+        Array have the correct shape and the expected data type.
+    order ("C","F","A","K", optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    usm_narray:
+        An array containing the element-wise truncated value of input array.
+        The returned array has the same data type as `x`.
+"""
+
+trunc = UnaryElementwiseFunc(
+    "trunc", ti._trunc_result_type, ti._trunc, _trunc_docstring
+)