Add batching support to sdk

.ado/ci.yml

@@ -58,7 +58,7 @@ jobs:
       displayName: Set Python version
 
     - script: |
-        pip install pytest pytest-azurepipelines pytest-cov
+        pip install pytest pytest-azurepipelines pytest-cov pytest-regressions
       displayName: Install pytest dependencies
 
     - script: |

azure-quantum/azure/quantum/target/microsoft/elements/dft/job.py

@@ -1,8 +1,13 @@
 import collections.abc
-from typing import Any, Dict, Union
+import logging
+from typing import Any, Dict, Union, Optional
 from azure.quantum.job import JobFailedWithResultsError
+from azure.quantum.job.base_job import BaseJob, ContentType
 from azure.quantum.job.job import Job, DEFAULT_TIMEOUT
 from azure.quantum._client.models import JobDetails
+from azure.quantum.workspace import Workspace
+
+logger = logging.getLogger(__name__)
 
 class MicrosoftElementsDftJob(Job):
     """
@@ -62,4 +67,105 @@ def _is_dft_failure_results(failure_results: Union[Dict[str, Any], str]) -> bool
                     and "error" in failure_results["results"][0] \
                     and isinstance(failure_results["results"][0]["error"], dict) \
                     and "error_type" in failure_results["results"][0]["error"] \
-                    and "error_message" in failure_results["results"][0]["error"]
+                    and "error_message" in failure_results["results"][0]["error"]
+
+    @classmethod
+    def from_input_data_container(
+        cls,
+        workspace: "Workspace",
+        name: str,
+        target: str,
+        input_data: bytes,
+        batch_input_blobs: Dict[str, bytes],
+        content_type: ContentType = ContentType.json,
+        blob_name: str = "inputData",
+        encoding: str = "",
+        job_id: str = None,
+        container_name: str = None,
+        provider_id: str = None,
+        input_data_format: str = None,
+        output_data_format: str = None,
+        input_params: Dict[str, Any] = None,
+        session_id: Optional[str] = None,
+        **kwargs
+    ) -> "BaseJob":
+        """Create a new Azure Quantum job based on a list of input_data.
+
+        :param workspace: Azure Quantum workspace to submit the input_data to
+        :type workspace: Workspace
+        :param name: Name of the job
+        :type name: str
+        :param target: Azure Quantum target
+        :type target: str
+        :param input_data: Raw input data to submit
+        :type input_data: Dict
+        :param blob_name: Dict of Input data json to gives a table of contents
+        :type batch_input_blobs: Dict
+        :param blob_name: Dict of QcSchema Data where the key is the blob name to store it in the container
+        :type blob_name: str
+        :param content_type: Content type, e.g. "application/json"
+        :type content_type: ContentType
+        :param encoding: input_data encoding, e.g. "gzip", defaults to empty string
+        :type encoding: str
+        :param job_id: Job ID, defaults to None
+        :type job_id: str
+        :param container_name: Container name, defaults to None
+        :type container_name: str
+        :param provider_id: Provider ID, defaults to None
+        :type provider_id: str
+        :param input_data_format: Input data format, defaults to None
+        :type input_data_format: str
+        :param output_data_format: Output data format, defaults to None
+        :type output_data_format: str
+        :param input_params: Input parameters, defaults to None
+        :type input_params: Dict[str, Any]
+        :param input_params: Input params for job
+        :type input_params: Dict[str, Any]
+        :return: Azure Quantum Job
+        :rtype: Job
+        """
+        # Generate job ID if not specified
+        if job_id is None:
+            job_id = cls.create_job_id()
+
+        # Create container if it does not yet exist
+        container_uri = workspace.get_container_uri(
+            job_id=job_id,
+            container_name=container_name
+        )
+        logger.debug(f"Container URI: {container_uri}")
+
+        # Upload Input Data
+        input_data_uri = cls.upload_input_data(
+            container_uri=container_uri,
+            input_data=input_data,
+            content_type=content_type,
+            blob_name=blob_name,
+            encoding=encoding,
+        )
+
+        # Upload data to container
+        for blob_name, input_data_item in batch_input_blobs.items():
+            cls.upload_input_data(
+                container_uri=container_uri,
+                input_data=input_data_item,
+                content_type=content_type,
+                blob_name=blob_name,
+                encoding=encoding,
+            )
+
+        # Create and submit job
+        return cls.from_storage_uri(
+            workspace=workspace,
+            job_id=job_id,
+            target=target,
+            input_data_uri=input_data_uri,
+            container_uri=container_uri,
+            name=name,
+            input_data_format=input_data_format,
+            output_data_format=output_data_format,
+            provider_id=provider_id,
+            input_params=input_params,
+            session_id=session_id,
+            **kwargs
+        )

azure-quantum/azure/quantum/target/microsoft/elements/dft/target.py

@@ -5,8 +5,11 @@
 from azure.quantum.target.target import Target
 from azure.quantum.workspace import Workspace
 from azure.quantum.target.params import InputParams
-from typing import Any, Dict, Type, Union
+from typing import Any, Dict, Type, Union, List
 from .job import MicrosoftElementsDftJob
+from pathlib import Path
+import copy
+import json
 
 
 class MicrosoftElementsDft(Target):
@@ -73,15 +76,161 @@ def submit(self,
         if shots is not None:
             warnings.warn("The 'shots' parameter is ignored in Microsoft Elements Dft job.")
 
-        return super().submit(
-            input_data=input_data,
-            name=name, 
-            shots=shots, 
-            input_params=input_params,
-            **kwargs
-        )
+        if isinstance(input_data, list):
+
+            qcschema_data = self._assemble_qcshema_from_files(input_data, input_params)
+
+            qcschema_blobs = {}
+            for i in range(len(qcschema_data)):
+                qcschema_blobs[f"inputData_{i}"] = self._encode_input_data(qcschema_data[i])
 
+            toc_str = self._create_table_of_contents(input_data, list(qcschema_blobs.keys()))
+            toc = self._encode_input_data(toc_str)
+
+            return self._get_job_class().from_input_data_container(
+                workspace=self.workspace,
+                name=name,
+                target=self.name,
+                input_data=toc,
+                batch_input_blobs=qcschema_blobs,
+                input_params={ 'numberOfFiles': len(qcschema_data), "inputFiles": list(qcschema_blobs.keys()), **input_params },
+                content_type=kwargs.pop('content_type', self.content_type),
+                encoding=kwargs.pop('encoding', self.encoding),
+                provider_id=self.provider_id,
+                input_data_format=kwargs.pop('input_data_format', 'microsoft.qc-schema.v1'),
+                output_data_format=kwargs.pop('output_data_format', self.output_data_format),
+                session_id=self.get_latest_session_id(),
+                **kwargs
+            )
+        else:
+            return super().submit(
+                input_data=input_data,
+                name=name, 
+                shots=shots, 
+                input_params=input_params,
+                **kwargs
+            )
+
+
+
+    @classmethod
+    def _assemble_qcshema_from_files(self, input_data: List[str], input_params: Dict) -> str:
+        """
+        Convert a list of files to a list of qcshema objects serialized in json.
+        """
+
+        qcshema_objects = []
+        for file in input_data:
+            file_path = Path(file)
+            if not file_path.exists():
+                raise FileNotFoundError(f"File {file} does not exist.")
+
+            file_data = file_path.read_text()
+            if file_path.suffix == '.xyz':
+                mol = self._xyz_to_qcschema_mol(file_data)
+                new_qcschema = self._new_qcshema( input_params, mol )
+                qcshema_objects.append(new_qcschema)
+            elif file_path.suffix == '.json':
+                if input_params is not None and len(input_params.keys()) > 0:
+                    warnings.warn('Input parameters were given along with a QcSchema file which contains parameters, using QcSchema parameters as is.')
+                with open(file_path, 'r') as f:
+                    qcshema_objects.append( json.load(f) )
+            else:
+                raise ValueError(f"File type '{file_path.suffix}' for file '{file_path}' is not supported. Please use xyz or QcSchema file formats.")
+
+        return qcshema_objects
+
+    @classmethod
+    def _new_qcshema( self, input_params: Dict[str,Any], mol: Dict[str,Any],  ) -> Dict[str, Any]:
+        """
+        Create a new default qcshema object.
+        """
+
+        if input_params.get("driver") == "go":
+            copy_input_params = copy.deepcopy(input_params)
+            copy_input_params["driver"] = "gradient"
+            new_object = {
+                "schema_name": "qcschema_optimization_input",
+                "schema_version": 1,
+                "initial_molecule": mol,
+            }
+            if copy_input_params.get("keywords") and copy_input_params["keywords"].get("geometryOptimization"):
+                new_object["keywords"] = copy_input_params["keywords"].pop("geometryOptimization")
+            new_object["input_specification"] = copy_input_params
+            return new_object
+        elif input_params.get("driver") == "bomd":
+            copy_input_params = copy.deepcopy(input_params)
+            copy_input_params["driver"] = "gradient"
+            new_object = {
+                "schema_name": "madft_molecular_dynamics_input",
+                "schema_version": 1,
+                "initial_molecule": mol,
+            }
+            if copy_input_params.get("keywords") and copy_input_params["keywords"].get("molecularDynamics"):
+                new_object["keywords"] = copy_input_params["keywords"].pop("molecularDynamics")
+            new_object["input_specification"] = copy_input_params
+            return new_object
+        else:
+            new_object = copy.deepcopy(input_params)
+            new_object.update({
+                "schema_name": "qcschema_input",
+                "schema_version": 1,
+                "molecule": mol,
+            })
+            return new_object
+
+
+    @classmethod
+    def _xyz_to_qcschema_mol(self, file_data: str ) -> Dict[str, Any]:
+        """
+        Convert xyz format to qcschema molecule.
+        """
+
+        lines = file_data.split("\n")
+        if len(lines) < 3:
+            raise ValueError("Invalid xyz format.")
+        n_atoms = int(lines.pop(0))
+        comment = lines.pop(0)
+        mol = {
+            "geometry": [],
+            "symbols": [],
+        }
+        for line in lines:
+            if line:
+                elements = line.split()
+                if len(elements) < 4:
+                    raise ValueError("Invalid xyz format.")
+                symbol, x, y, z = elements
+                mol["symbols"].append(symbol)
+                mol["geometry"] += [float(x), float(y), float(z)]
+            else:
+                break
+
+        if len(mol["symbols"]) != n_atoms:
+            raise ValueError("Number of inputs does not match the number of atoms in xyz file.")
+
+        return mol
 
     @classmethod
     def _get_job_class(cls) -> Type[Job]:
         return MicrosoftElementsDftJob
+
+    @classmethod
+    def _create_table_of_contents(cls, input_files: List[str], input_blobs: List[str]) -> Dict[str,Any]:
+        """Create the table of contents for a batched job that contains a description of file and the mapping between the file names and the blob names"""
+
+        assert len(input_files) == len(input_blobs), "Internal error: number of blobs is not that same as the number of files."
+
+        toc = []
+        for i in range(len(input_files)):
+            toc.append( 
+                {
+                    "inputFileName": input_files[i],
+                    "qcschemaBlobName": input_blobs[i],
+                }
+            )
+
+        return {
+            "description": "This files contains the mapping between the xyz file name that were submitted and the qcschema blobs that are used for the calculation.",
+            "tableOfContents": toc,
+        }

azure-quantum/environment.yml

@@ -6,5 +6,6 @@ dependencies:
   - python=3.9
   - pip>=22.3.1
   - pytest>=7.1.2
+  - pytest-regressions
   - pip:
     - -e .[all]