# OR-1 CPU - dfasm Assembler Architecture

Covers the `asm/` package: pipeline structure, IR design, pass architecture, code generation modes, and key implementation decisions.

See `architecture-overview.md` for the target hardware model.
See `dfasm-primer.md` for the language itself.

## Role

The assembler translates dfasm source into emulator-ready configuration objects (`PEConfig`, `SMConfig`, seed tokens) or a hardware-faithful bootstrap token sequence. It bridges the gap between human-authored dataflow graph programs and the structures the emulator (and eventually hardware) consumes.

The assembler does NOT (currently) optimize. It does not reorder instructions for performance, fuse operations, or eliminate redundant sub-graphs. It is a faithful translator: the graph you write is the graph you get. Future optimization passes may be inserted between resolve and place, but the current pipeline is intentionally thin. Correctness first, cleverness later.

## Pipeline Overview

Seven stages, each a pure function from `IRGraph → IRGraph` (or `IRGraph → output`).

The pipeline is:

```
dfasm source
    │
    ▼
  Parse       Lark/Earley parser, dfasm.lark grammar
    │          → concrete syntax tree (CST)
    ▼
  Lower       CST → IRGraph (nodes, edges, regions, data defs,
    │            macro defs, macro calls, call sites)
    │          → name qualification, scope creation
    ▼
  Expand      macro expansion + function call wiring
    │          → clone macro bodies, substitute params, evaluate
    │            const expressions, wire cross-context call edges
    ▼
  Resolve     validate edge endpoints, detect scope violations
    │          → "did you mean" suggestions via Levenshtein distance
    ▼
  Place       assign PEs to unplaced nodes
    │          → greedy bin-packing with locality heuristic
    ▼
  Allocate    assign IRAM offsets, context slots, resolve addresses
    │          → dyadic-first layout, per-PE context scoping
    ▼
  Codegen     emit PEConfig/SMConfig + seeds (direct mode)
              or SM init → IRAM writes → seeds (token mode)
```

Each pass returns a new `IRGraph`. Graphs are never mutated after construction, each pass produces a fresh copy with the new information filled in. Errors accumulate in `IRGraph.errors` rather than failing fast, so the assembler reports all problems in a single pass rather than forcing the programmer to fix them one at a time.

The public API orchestrates the pipeline and raises `ValueError` if any stage produces errors:

```python
assemble(source: str) -> AssemblyResult       # direct mode
assemble_to_tokens(source: str) -> list       # token stream mode
round_trip(source: str) -> str                # parse → lower → serialize
serialize_graph(graph: IRGraph) -> str        # IRGraph → dfasm at any stage
```

## IR Types (`ir.py`)

### Core Types

| Type | Fields | Purpose |
|------|--------|---------|
| `IRNode` | name, opcode, dest_l, dest_r, const, pe, iram_offset, ctx, loc, args, sm_id, seed | Single instruction in the dataflow graph. `name` and `const` can be `ParamRef` or `ConstExpr` in macro templates. |
| `IREdge` | source, dest, port, source_port, port_explicit, ctx_override, loc | Connection between two nodes. `ctx_override` marks cross-context call edges (ctx_mode=01). `port_explicit` tracks whether port was user-specified. |
| `IRGraph` | nodes, edges, regions, data_defs, system, errors, macro_defs, macro_calls, raw_call_sites, call_sites, builtin_line_offset | Complete program representation. Macro-related fields are populated by lower and consumed by expand. |
| `IRRegion` | tag, kind, body (IRGraph), loc | Nested scope (FUNCTION, LOCATION, or MACRO) |
| `IRDataDef` | name, sm_id, cell_addr, value, loc | SM cell initialisation |
| `SystemConfig` | pe_count, sm_count, iram_capacity, ctx_slots, loc | Hardware configuration from `@system` pragma (defaults: iram=128, ctx=16) |
| `MacroDef` | name, params, body (IRGraph), repetition_blocks, loc | Macro definition: formal parameters + template body with `ParamRef` placeholders |
| `MacroParam` | name, variadic | Formal macro parameter. `variadic=True` collects remaining args. |
| `ParamRef` | param, prefix, suffix | Placeholder for a macro parameter. Supports token pasting via prefix/suffix. |
| `ConstExpr` | expression, params, loc | Compile-time arithmetic expression (e.g., `base + _idx + 1`) |
| `IRRepetitionBlock` | body (IRGraph), variadic_param, loc | Repetition block (`@each`) expanded once per variadic argument |
| `IRMacroCall` | name, positional_args, named_args, loc | Macro invocation, consumed by expand pass |
| `CallSiteResult` | func_name, input_args, output_dests, loc | Intermediate call data from lower, consumed by expand |
| `CallSite` | func_name, call_id, input_edges, trampoline_nodes, free_ctx_nodes, loc | Processed call site metadata for per-call-site context allocation |

### Destination Representation

Destinations evolve through the pipeline:

1. **After lower**: `NameRef(name, port)` - symbolic, unresolved
2. **After allocate**: `ResolvedDest(name, addr)` - concrete `Addr(a, port, pe)` with IRAM offset, port, and target PE

This two-stage resolution means early passes can work with symbolic names while later passes have concrete hardware addresses, while maintaining the names for debug clarity.
### Graph Traversal Utilities

`IRGraph` provides recursive traversal functions for working with nested regions:

- `iter_all_subgraphs()`: depth-first traversal of graph + all region bodies
- `collect_all_nodes()`: flatten nodes from graph and all nested regions
- `collect_all_nodes_and_edges()`: flatten both
- `collect_all_data_defs()`: flatten data definitions
- `update_graph_nodes()`: recursively update nodes while preserving region structure

## Pass Details

### Parse

Uses the Lark library with the Earley parser algorithm. The formal grammar is defined in `dfasm.lark`. Earley is required because the grammar has ambiguities between `location_dir` (a bare qualified reference) and `weak_edge` (outputs before opcode) that require context-sensitive resolution.

The parser produces a concrete syntax tree (CST), a Lark `Tree` objects with `Token` terminals.
### Lower (`lower.py`)

A Lark `Transformer` that walks the CST bottom-up, converting each grammar rule into IR types.

**Key transformations:**

- `inst_def` -> `IRNode` with opcode, const, PE placement, named args
- `plain_edge` -> `IREdge` with source, dest, port qualifiers
- `strong_edge` / `weak_edge` -> anonymous `IRNode` + input/output
  `IREdge` set (creates a `CompositeResult`)
- `func_def` → `IRRegion(kind=FUNCTION)` with a nested `IRGraph` body
- `macro_def` → `MacroDef` with `ParamRef` placeholders in body template
- `macro_call` → `IRMacroCall` with positional/named arguments
- `call_stmt` → `CallSiteResult` with function name, input args, output dests
- `location_dir` -> `IRRegion(kind=LOCATION)`: subsequent statements
  are collected into its body during post-processing
- `data_def` -> `IRDataDef` with SM placement and cell address
- `system_pragma` → `SystemConfig` (stored on the transformer, attached
  to the final `IRGraph`)

**Name qualification:**

Labels (`&name`) inside function regions are qualified with the function scope: `&add` inside `$main` becomes `$main.&add`.

**Opcode mapping (`opcodes.py`):**

Mnemonic strings from the grammar are mapped to `ALUOp` or `MemOp` enum values via `MNEMONIC_TO_OP`. A complication: Python `IntEnum` sub-classes can share numeric values across types (`ArithOp.ADD == 0 == MemOp.READ`), so the reverse mapping and set membership tests use type-aware collections (`TypeAwareOpToMnemonicDict`, `TypeAwareMonadicOpsSet`) that key on `(type, value)` tuples internally.

### Expand (`expand.py`)

Processes macro definitions, macro invocations, and function call sites. This pass bridges the gap between the template-based IR from lowering and the concrete IR that resolve expects.

**Macro expansion:**

1. Collect all `MacroDef` entries from the graph (including built-in macros prepended to every program)
2. For each `IRMacroCall`, clone the macro's body template
3. Substitute `ParamRef` placeholders with actual argument values in all contexts:
   - **Const fields**: literal value substitution
   - **Edge endpoints**: node reference substitution
   - **Node names**: token pasting with prefix/suffix
   - **Opcode position**: resolve mnemonic string via `MNEMONIC_TO_OP` to concrete `ALUOp`/`MemOp`
   - **Placement qualifiers**: resolve `"pe0"` → `0` (via `PlacementRef`)
   - **Port qualifiers**: resolve `"L"` → `Port.L` (via `PortRef`)
   - **Context slot qualifiers**: resolve integer values (via `CtxSlotRef`)
4. Evaluate `ConstExpr` arithmetic expressions (supports `+`, `-`, `*`, `//` on integers and `_idx`)
5. Expand `IRRepetitionBlock` entries once per variadic argument, binding `_idx` to the iteration index
6. Qualify expanded names with scope prefixes: `#macroname_N.&label` for top-level, `$func.#macro_N.&label` inside functions

**Macro `@ret` wiring:**

After body expansion, `@ret` / `@ret_name` markers in macro edge destinations are replaced with concrete node references from the call site's output list (`IRMacroCall.output_dests`). This is pure edge rewriting — no trampolines, no cross-context routing, no `free_ctx`. Macros inline into the caller's context.

**Function call wiring:**

1. For each `CallSiteResult` from lowering, create a `CallSite` with a unique `call_id`
2. Generate trampoline `PASS` nodes for return routing
3. Create `IREdge` entries with `ctx_override=True` for cross-context argument passing (becomes `ctx_mode=01` in codegen)
4. Generate `FREE_CTX` nodes for context teardown on call completion
5. Wire `@ret` / `@ret_name` synthetic nodes for return paths (function `@ret` is distinct from macro `@ret` — functions create trampolines with context management)

**Post-conditions:**

After expansion, the IR contains only concrete `IRNode`/`IREdge` entries. No `ParamRef` placeholders, no `MacroDef` regions, no `IRMacroCall` entries remain.

### Resolve (`resolve.py`)

Validates that all edge endpoints exist in the flattened namespace.

**Process:**

1. Flatten all nodes from the graph and all nested regions into a single namespace
2. Build a scope map: qualified name -> defining scope
3. For each edge, check that both source and dest exist
4. Detect cross-function label references (a `&label` in `$main` cannot reference a `&label` in `$other`) and flag as SCOPE errors
5. For undefined references, compute Levenshtein distance against all known names and suggest the closest match ("did you mean `&addr`?")

Resolve does not modify nodes, it only appends errors.

### Place (`place.py`)

Assigns PE IDs to nodes that don't have explicit placement.

**Explicit placements** (from `|pe0` qualifiers in source) are validated first. 
Reject any `pe >= pe_count`.

**Auto-placement algorithm** for unplaced nodes (greedy, insertion order):

1. For each unplaced node, find its connected neighbours via edges
2. Count PE occurrences among placed neighbours (locality heuristic)
3. Sort candidate PEs by: most neighbours (descending), 
   then most remaining IRAM capacity (tie-break)
4. Place on the first PE with room for both IRAM and context slots
5. If no PE fits, record a placement error with per-PE utilization breakdown

**IRAM cost model:**

- Dyadic instruction: 2 slots (instruction + matching store entry)
- Monadic instruction: 1 slot

**System config inference:**

If no `@system` pragma is provided, the placer infers `pe_count` from the highest explicit PE ID and uses defaults for IRAM capacity (128) and context slots (16).

### Allocate (`allocate.py`)

Assigns three things per node: IRAM offset, context slot, and resolved destinations.

**IRAM layout (per PE):**

Dyadic instructions are packed at low offsets (0..D-1), monadic at higher offsets (D..D+M-1). This matches the hardware contract in `pe-design.md`: the token's offset field doubles as the matching store entry for dyadic instructions, so they must occupy the dense low range.

**Context slot assignment (per PE):**

Each function scope gets a distinct context slot. The root scope (top-level) always gets slot 0. Additional scopes get slots 1, 2, ... in order of first appearance. Overflow beyond `ctx_slots` is a RESOURCE error.

**Destination resolution:**

For each node, outgoing edges are resolved to `ResolvedDest` objects containing concrete `Addr(a=iram_offset, port=Port.L|R, pe=target_pe)`. The allocator handles:

- Single outgoing edge -> `dest_l`
- Two outgoing edges -> `dest_l` + `dest_r` (distinguished by `source_port` qualifier or positional order)
- Port conflicts (duplicate ports, mixed explicit/implicit) -> PORT error

**SM ID assignment:**

For `MemOp` nodes, the allocator assigns the target SM ID. Single-SM systems default to `sm_id=0`. Multi-SM systems with ambiguous targets produce a RESOURCE error.

### Codegen (`codegen.py`)

Two output modes from the same allocated `IRGraph`:

**Direct mode** (`generate_direct() → AssemblyResult`):

Produces immediately usable emulator configuration:

- `pe_configs`: list of `PEConfig` with populated IRAM (ALUInst/SMInst by offset), context slot count, and route restrictions
- `sm_configs`: list of `SMConfig` with initial cell values from data definitions
- `seed_tokens`: `CMToken` for each strongly-connected `CONST` node with no incoming edges

Route restrictions are computed by scanning all edges from each PE to determine which other PEs and SMs it can reach. Self-routes are always included.

**Token stream mode** (`generate_tokens() → list`):

Produces a hardware-faithful bootstrap sequence:

1. **SM init tokens**: `SMToken(op=WRITE)` for each data definition
2. **IRAMWrite tokens**: `IRAMWrite` tokens per PE with instructions
3. **Seed tokens**: same `CMToken` list as direct mode

This ordering mirrors what the hardware bootstrap would do: initialize structure memory, load instruction memory, then inject the initial tokens that start execution.

Both modes reuse the same internal logic. Token stream mode calls `generate_direct()` internally and then re-formats the result.

## Error Handling (`errors.py`)

Errors are structured with category, source location, message, and optional suggestions.

| Category | Stage | Examples |
|----------|-------|---------|
| PARSE | lower | unknown opcode, malformed grammar |
| NAME | resolve | undefined node reference |
| SCOPE | resolve, lower | cross-function label reference, duplicate definition |
| PLACEMENT | place | PE ID out of range, capacity exceeded |
| RESOURCE | allocate | IRAM overflow, context slot overflow, missing SM target |
| ARITY | lower | wrong operand count |
| PORT | allocate | port conflicts, missing destinations |
| MACRO | expand | undefined macro, wrong argument count, expansion failure |
| CALL | expand | undefined function, missing return path, call wiring error |
| UNREACHABLE | (future) | unused nodes |
| VALUE | lower | out-of-range literals |

Error formatting follows a Rust-style pattern:

```
error[SCOPE]: Duplicate label '&add' in function '$main'
 --> line 5, column 3
  |
5 |   &add <| sub
  |   ^^^
  = help: First defined at line 2
```

## Serialization and Round-Tripping (`serialize.py`)

The serializer emits valid dfasm source from an `IRGraph` at any pipeline stage. This enables:

- **Round-trip testing**: `source → parse → lower → serialize → source'` verifies that the parser and lowering pass preserve the program
- **IR inspection**: dump the graph after any pass to see what the assembler is doing
- **Code generation from IR**: future tools could construct `IRGraph` objects programmatically and serialize them to dfasm

The serializer unqualifies names inside function regions (strips the `$func.` prefix), preserves port and placement qualifiers, and formats values as hex (if > 255) or decimal.

## Future Work

- **Optimization passes** between resolve and place: dead node elimination, constant folding, sub-graph deduplication
- **Wider placement heuristics**: graph partitioning, min-cut algorithms, or profile-guided placement for larger programs
- **Incremental reassembly**: modify part of the graph and re-run only affected passes
- **Hardware encoding pass**: translate ALUInst/SMInst to bit-level instruction words for actual IRAM loading
- **Conditional macro expansion**: the current macro system supports variadic repetition, constant arithmetic, opcode parameters, parameterized qualifiers, `@ret` output wiring (including positional `@ret` in variadic repetition blocks), and nested macro invocation (depth limit 32), but not conditionals within macros
- **Generic variadic reduction**: a single `#reduce` macro that infers tree depth from variadic argument count (requires conditional expansion to handle non-power-of-2 inputs)