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compiler: Implement matrix multiplication
LLVM will take care of optimising the loops. This was still unnecessarily painful; implementing generics and implementing this in ARTIQ Python looks very attractive right now.
This commit is contained in:
David Nadlinger
parent
0da4a61d99
commit
ef260adca8
@ -527,7 +527,7 @@ class ARTIQIRGenerator(algorithm.Visitor):
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if num_dims > 1:
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old_shape = self.append(ir.GetAttr(value, "shape"))
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lengths = [self.append(ir.GetAttr(old_shape, i)) for i in range(1, num_dims)]
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new_shape = self.append(ir.Alloc(lengths, types.TTuple(old_shape.type.elts[1:])))
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new_shape = self._make_array_shape(lengths)
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stride = reduce(
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lambda l, r: self.append(ir.Arith(ast.Mult(loc=None), l, r)),
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@ -1444,7 +1444,7 @@ class ARTIQIRGenerator(algorithm.Visitor):
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result_type = builtins.TArray(elt, types.TValue(len(shape.type.elts)))
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return self.append(ir.Alloc([buffer, shape], result_type))
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def _make_array_binop(self, name, op, result_type, lhs_type, rhs_type):
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def _make_array_binop(self, name, result_type, lhs_type, rhs_type, body_gen):
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try:
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result = ir.Argument(result_type, "result")
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lhs = ir.Argument(lhs_type, "lhs")
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@ -1461,8 +1461,8 @@ class ARTIQIRGenerator(algorithm.Visitor):
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ret=builtins.TNone())
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env_args = [ir.EnvironmentArgument(self.current_env.type, "ARG.ENV")]
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# TODO: What to use for loc?
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func = ir.Function(typ, name, env_args + args, loc=None)
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old_loc, self.current_loc = self.current_loc, None
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func = ir.Function(typ, name, env_args + args)
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func.is_internal = True
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func.is_generated = True
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self.functions.append(func)
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@ -1474,36 +1474,12 @@ class ARTIQIRGenerator(algorithm.Visitor):
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old_final_branch, self.final_branch = self.final_branch, None
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old_unwind, self.unwind_target = self.unwind_target, None
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shape = self.append(ir.GetAttr(lhs, "shape"))
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rhs_shape = self.append(ir.GetAttr(rhs, "shape"))
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self._make_check(
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self.append(ir.Compare(ast.Eq(loc=None), shape, rhs_shape)),
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lambda: self.alloc_exn(
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builtins.TException("ValueError"),
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ir.Constant("operands could not be broadcast together",
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builtins.TStr())))
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# We assume result has correct shape; could just pass buffer pointer as well.
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result_buffer = self.append(ir.GetAttr(result, "buffer"))
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lhs_buffer = self.append(ir.GetAttr(lhs, "buffer"))
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rhs_buffer = self.append(ir.GetAttr(rhs, "buffer"))
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num_total_elts = self._get_total_array_len(shape)
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def body_gen(index):
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l = self.append(ir.GetElem(lhs_buffer, index))
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r = self.append(ir.GetElem(rhs_buffer, index))
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self.append(
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ir.SetElem(result_buffer, index, self.append(ir.Arith(op, l, r))))
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return self.append(
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ir.Arith(ast.Add(loc=None), index, ir.Constant(1, self._size_type)))
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self._make_loop(
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ir.Constant(0, self._size_type), lambda index: self.append(
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ir.Compare(ast.Lt(loc=None), index, num_total_elts)), body_gen)
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body_gen(result, lhs, rhs)
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self.append(ir.Return(ir.Constant(None, builtins.TNone())))
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return func
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finally:
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self.current_loc = old_loc
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self.current_function = old_func
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self.current_block = old_block
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self.final_branch = old_final_branch
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@ -1518,8 +1494,9 @@ class ARTIQIRGenerator(algorithm.Visitor):
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# rpc_tag is used to turn element types into mangled names for no
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# particularly good reason apart from not having to invent yet another
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# string representation.
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return (ir.rpc_tag(typ["elt"], name_error).decode() +
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str(typ["num_dims"].find().value))
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if builtins.is_array(typ):
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return mangle_name(typ["elt"]) + str(typ["num_dims"].find().value)
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return ir.rpc_tag(typ, name_error).decode()
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return "_".join(mangle_name(t) for t in types)
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@ -1531,8 +1508,41 @@ class ARTIQIRGenerator(algorithm.Visitor):
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type(op).__name__,
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self._mangle_arrayop_types([result_type, lhs_type, rhs_type]))
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if name not in self.array_binop_funcs:
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def body_gen(result, lhs, rhs):
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# TODO: Move into caller for correct location information (or pass)?
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shape = self.append(ir.GetAttr(lhs, "shape"))
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rhs_shape = self.append(ir.GetAttr(rhs, "shape"))
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self._make_check(
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self.append(ir.Compare(ast.Eq(loc=None), shape, rhs_shape)),
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lambda: self.alloc_exn(
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builtins.TException("ValueError"),
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ir.Constant("operands could not be broadcast together",
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builtins.TStr())))
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# We assume result has correct shape; could just pass buffer pointer
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# as well.
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result_buffer = self.append(ir.GetAttr(result, "buffer"))
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lhs_buffer = self.append(ir.GetAttr(lhs, "buffer"))
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rhs_buffer = self.append(ir.GetAttr(rhs, "buffer"))
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num_total_elts = self._get_total_array_len(shape)
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def loop_gen(index):
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l = self.append(ir.GetElem(lhs_buffer, index))
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r = self.append(ir.GetElem(rhs_buffer, index))
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self.append(
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ir.SetElem(result_buffer, index, self.append(ir.Arith(op, l,
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r))))
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return self.append(
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ir.Arith(ast.Add(loc=None), index,
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ir.Constant(1, self._size_type)))
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self._make_loop(
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ir.Constant(0, self._size_type), lambda index: self.append(
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ir.Compare(ast.Lt(loc=None), index, num_total_elts)), loop_gen)
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self.array_binop_funcs[name] = self._make_array_binop(
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name, op, result_type, lhs_type, rhs_type)
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name, result_type, lhs_type, rhs_type, body_gen)
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return self.array_binop_funcs[name]
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def _invoke_arrayop(self, func, params):
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@ -1545,14 +1555,162 @@ class ARTIQIRGenerator(algorithm.Visitor):
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self.append(ir.Invoke(func, params, {}, after_invoke, self.unwind_target))
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self.current_block = after_invoke
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def _get_array_offset(self, shape, indices):
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last_stride = None
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result = indices[0]
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for dim, index in zip(shape[:-1], indices[1:]):
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result = self.append(ir.Arith(ast.Mult(loc=None), result, dim))
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result = self.append(ir.Arith(ast.Add(loc=None), result, index))
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return result
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def _get_matmult(self, result_type, lhs_type, rhs_type):
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name = "_array_MatMult_" + self._mangle_arrayop_types(
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[result_type, lhs_type, rhs_type])
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if name not in self.array_binop_funcs:
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def body_gen(result, lhs, rhs):
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assert builtins.is_array(result.type), \
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"vec @ vec should have been normalised into array result"
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# We assume result has correct shape; could just pass buffer pointer
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# as well.
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result_buffer = self.append(ir.GetAttr(result, "buffer"))
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lhs_buffer = self.append(ir.GetAttr(lhs, "buffer"))
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rhs_buffer = self.append(ir.GetAttr(rhs, "buffer"))
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num_rows, num_summands, _, num_cols = self._get_matmult_shapes(lhs, rhs)
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elt = result.type["elt"].find()
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env_type = ir.TEnvironment("loop", {"$total": elt})
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env = self.append(ir.Alloc([], env_type))
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def row_loop(row_idx):
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lhs_base_offset = self.append(
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ir.Arith(ast.Mult(loc=None), row_idx, num_summands))
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lhs_base = self.append(ir.Offset(lhs_buffer, lhs_base_offset))
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result_base_offset = self.append(
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ir.Arith(ast.Mult(loc=None), row_idx, num_cols))
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result_base = self.append(
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ir.Offset(result_buffer, result_base_offset))
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def col_loop(col_idx):
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rhs_base = self.append(ir.Offset(rhs_buffer, col_idx))
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self.append(
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ir.SetLocal(env, "$total", ir.Constant(elt.zero(), elt)))
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def sum_loop(sum_idx):
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lhs_elem = self.append(ir.GetElem(lhs_base, sum_idx))
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rhs_offset = self.append(
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ir.Arith(ast.Mult(loc=None), sum_idx, num_cols))
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rhs_elem = self.append(ir.GetElem(rhs_base, rhs_offset))
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product = self.append(
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ir.Arith(ast.Mult(loc=None), lhs_elem, rhs_elem))
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prev_total = self.append(ir.GetLocal(env, "$total"))
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total = self.append(
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ir.Arith(ast.Add(loc=None), prev_total, product))
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self.append(ir.SetLocal(env, "$total", total))
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return self.append(
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ir.Arith(ast.Add(loc=None), sum_idx,
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ir.Constant(1, self._size_type)))
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self._make_loop(
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ir.Constant(0, self._size_type), lambda index: self.append(
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ir.Compare(ast.Lt(loc=None), index, num_summands)),
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sum_loop)
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total = self.append(ir.GetLocal(env, "$total"))
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self.append(ir.SetElem(result_base, col_idx, total))
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return self.append(
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ir.Arith(ast.Add(loc=None), col_idx,
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ir.Constant(1, self._size_type)))
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self._make_loop(
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ir.Constant(0, self._size_type), lambda index: self.append(
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ir.Compare(ast.Lt(loc=None), index, num_cols)), col_loop)
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return self.append(
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ir.Arith(ast.Add(loc=None), row_idx,
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ir.Constant(1, self._size_type)))
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self._make_loop(
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ir.Constant(0, self._size_type), lambda index: self.append(
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ir.Compare(ast.Lt(loc=None), index, num_rows)), row_loop)
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self.array_binop_funcs[name] = self._make_array_binop(
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name, result_type, lhs_type, rhs_type, body_gen)
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return self.array_binop_funcs[name]
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def _get_matmult_shapes(self, lhs, rhs):
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lhs_shape = self.append(ir.GetAttr(lhs, "shape"))
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if lhs.type["num_dims"].value == 1:
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lhs_shape_outer = ir.Constant(1, self._size_type)
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lhs_shape_inner = self.append(ir.GetAttr(lhs_shape, 0))
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else:
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lhs_shape_outer = self.append(ir.GetAttr(lhs_shape, 0))
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lhs_shape_inner = self.append(ir.GetAttr(lhs_shape, 1))
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rhs_shape = self.append(ir.GetAttr(rhs, "shape"))
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if rhs.type["num_dims"].value == 1:
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rhs_shape_inner = self.append(ir.GetAttr(rhs_shape, 0))
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rhs_shape_outer = ir.Constant(1, self._size_type)
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else:
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rhs_shape_inner = self.append(ir.GetAttr(rhs_shape, 0))
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rhs_shape_outer = self.append(ir.GetAttr(rhs_shape, 1))
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return lhs_shape_outer, lhs_shape_inner, rhs_shape_inner, rhs_shape_outer
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def _make_array_shape(self, dims):
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return self.append(ir.Alloc(dims, types.TTuple([self._size_type] * len(dims))))
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def _emit_matmult(self, node, left, right):
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# TODO: Also expose as numpy.dot.
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lhs = self.visit(left)
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rhs = self.visit(right)
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num_rows, lhs_inner, rhs_inner, num_cols = self._get_matmult_shapes(lhs, rhs)
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self._make_check(
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self.append(ir.Compare(ast.Eq(loc=None), lhs_inner, rhs_inner)),
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lambda lhs_inner, rhs_inner: self.alloc_exn(
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builtins.TException("ValueError"),
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ir.Constant(
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"inner dimensions for matrix multiplication do not match ({0} vs. {1})",
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builtins.TStr()), lhs_inner, rhs_inner),
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params=[lhs_inner, rhs_inner],
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loc=node.loc)
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result_shape = self._make_array_shape([num_rows, num_cols])
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final_type = node.type.find()
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if not builtins.is_array(final_type):
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elt = node.type
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result_dims = 0
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else:
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elt = final_type["elt"]
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result_dims = final_type["num_dims"].value
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result = self._allocate_new_array(elt, result_shape)
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func = self._get_matmult(result.type, left.type, right.type)
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self._invoke_arrayop(func, [result, lhs, rhs])
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if result_dims == 2:
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return result
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result_buffer = self.append(ir.GetAttr(result, "buffer"))
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if result_dims == 1:
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shape = self._make_array_shape(
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[num_cols if lhs.type["num_dims"].value == 1 else num_rows])
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return self.append(ir.Alloc([result_buffer, shape], node.type))
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return self.append(ir.GetElem(result_buffer, ir.Constant(0, self._size_type)))
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def visit_BinOpT(self, node):
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if builtins.is_array(node.type):
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if isinstance(node.op, ast.MatMult):
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return self._emit_matmult(node, node.left, node.right)
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elif builtins.is_array(node.type):
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lhs = self.visit(node.left)
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rhs = self.visit(node.right)
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# Array op implementation will check for matching shape.
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# TODO: Broadcasts; select the widest shape.
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# TODO: Detect and special-case matrix multiplication.
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shape = self.append(ir.GetAttr(lhs, "shape"))
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result = self._allocate_new_array(node.type.find()["elt"], shape)
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25
artiq/test/lit/integration/array_matmult.py
Normal file
25
artiq/test/lit/integration/array_matmult.py
Normal file
@ -0,0 +1,25 @@
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# RUN: %python -m artiq.compiler.testbench.jit %s
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mat23 = array([[1, 2, 3], [4, 5, 6]])
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mat32 = array([[1, 2], [3, 4], [5, 6]])
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vec2 = array([1, 2])
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vec3 = array([1, 2, 3])
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assert vec3 @ vec3 == 14
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a = mat23 @ mat32
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assert a.shape == (2, 2)
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assert a[0][0] == 22
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assert a[0][1] == 28
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assert a[1][0] == 49
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assert a[1][1] == 64
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b = mat23 @ vec3
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assert b.shape == (2,)
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assert b[0] == 14
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assert b[1] == 32
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b = vec3 @ mat32
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assert b.shape == (2,)
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assert b[0] == 22
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assert b[1] == 28
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