Create the new benchmark database (#5839)

As defined in https://fburl.com/gdoc/pczummt2, this doesn't create the
table, but I want to start keeping the create table SQL query on git

### Testing

Manually run the query to create the table on CH
https://console.clickhouse.cloud/services/c9b76950-2cf3-4fa0-93bb-94a65ff5f27d/console/database/benchmark/table/oss_ci_benchmark_v3

torchci/clickhouse_queries/oss_ci_benchmark_v3/query.sql

@@ -0,0 +1,107 @@
+-- This query creates the oss_ci_benchmark_v3 table on ClickHouse
+CREATE TABLE benchmark.oss_ci_benchmark_v3 (
+    -- Metadata
+    `timestamp` UInt64,
+    `schema_version` String DEFAULT 'v3',
+    `name` String,
+    -- About the change
+    `repo` String DEFAULT 'pytorch/pytorch',
+    `head_branch` String,
+    `head_sha` String,
+    `workflow_id` UInt64,
+    `run_attempt` UInt32,
+    `job_id` UInt64,
+    -- (optional) Service Lab id if the result comes from there
+    `servicelab_experiment_id` UInt64 DEFAULT '0',
+    `servicelab_trial_id` UInt64 DEFAULT '0',
+    -- The raw records on S3
+    `s3_path` String,
+    -- About the devices where the benchmark runs. There could be more than one
+    -- runner (distributed benchmark)
+    --  name, the optional name of the runner
+    --  type, the runner type or label, i.e. linux.aws.a100, or android S22
+    --  cpu_info, i.e. x86_64 or arm
+    --  cpu_count, the number of CPU cores
+    --  mem_info, TBD
+    --  avail_mem_in_gb, the amount of available memory (GB)
+    --  gpu_info, i.e. NVIDIA A100 or mps
+    --  gpu_count, the number of GPU devices
+    --  gpu_mem_info, TBD
+    --  avail_gpu_mem_in_gb, the total amount of available GPU memory (GB)
+    --  extra_info, any extra piece of information in key/value format
+    `runners` Array(
+        Tuple(
+            name String,
+            type String,
+            cpu_info String,
+            cpu_count UInt32,
+            mem_info String,
+            avail_mem_in_gb UInt32,
+            gpu_info String,
+            gpu_count UInt32,
+            gpu_mem_info String,
+            avail_gpu_mem_in_gb UInt32,
+            extra_info Map(String, String)
+        )
+    ),
+    -- About the benchmark
+    --   name, the name of the benchmark, i.e. cudagraphs
+    --   mode, training or inference
+    --   dtype, the quantization / dtype used by the benchmark
+    `benchmark` Tuple(
+        name String,
+        mode String,
+        dtype String,
+        extra_info Map(String, String)
+    ),
+    -- About the model
+    --  name, the model name
+    --  type, open field, this could be a model, a custom layer, or a fused ops
+    --  backend, the optional name of any delegation backend used by the model, i.e. XNNPACK
+    --  origins, where it comes from, i.e. TorchBench. A model could be in multiple sources
+    --  extra_info, any extra piece of information in key/value format
+    `model` Tuple (
+        name String,
+        type String,
+        backend String,
+        origins Array(String),
+        extra_info Map(String, String)
+    ),
+    -- About the inputs of the benchmark. Use a map keyed by input name here as there could
+    -- be more than one
+    `inputs` Map(
+        String,
+        Tuple(dtype String, extra_info Map(String, String))
+    ),
+    -- About important dependencies used by the benchmark, obviously there can be more than
+    -- one, i.e. HF
+    `dependencies` Map(
+        String,
+        Tuple(
+            `repo` String,
+            `branch` String,
+            `sha` String,
+            `version` String,
+            extra_info Map(String, String)
+        )
+    ),
+    -- About the metric
+    --  name, the metric name
+    --  benchmark_values, the benchmark values, using a list here to cover the case where there are multple runs
+    `metric` Tuple(
+        name String,
+        benchmark_values Array(Float32),
+        target_value Float32,
+        extra_info Map(String, String)
+    ),
+) ENGINE = MergeTree('/clickhouse/tables/{uuid}/{shard}', '{replica}')
+ORDER BY
+    (
+        timestamp,
+        head_branch,
+        head_sha,
+        workflow_id,
+        job_id,
+        servicelab_experiment_id,
+        servicelab_trial_id
+    ) SETTINGS index_granularity = 8192