# Copyright 2023 The JAX Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Module for calling pallas functions from JAX.""" from __future__ import annotations from collections.abc import Callable, Mapping, Sequence import contextlib import enum from functools import partial, reduce import types from typing import Any from jax._src import api import jax._src.lax as lax from jax._src import ad_util from jax._src import api_util from jax._src import checkify from jax._src import config from jax._src import core as jax_core from jax._src import effects from jax._src import hijax from jax._src import linear_util as lu from jax._src import state from jax._src.traceback_util import api_boundary from jax._src import tree_util from jax._src import typing as jax_typing from jax._src.frozen_dict import FrozenDict from jax._src.interpreters import ad from jax._src.interpreters import batching from jax._src.interpreters import mlir from jax._src.interpreters import partial_eval as pe from jax._src.pallas import core as pallas_core from jax._src.pallas import hlo_interpreter from jax._src.pallas import primitives from jax._src.state import discharge as state_discharge from jax._src.state import types as state_types from jax._src.util import ( safe_map, safe_zip, split_list, tuple_insert, unzip2, ) from jax._src import numpy as jnp map, unsafe_map = safe_map, map zip, unsafe_zip = safe_zip, zip BlockMapping = pallas_core.BlockMapping GridMapping = pallas_core.GridMapping no_block_spec = pallas_core.no_block_spec CostEstimate = pallas_core.CostEstimate Backend = pallas_core.Backend CompilerParams = pallas_core.CompilerParams # See the docstring for GridMapping for the calling convention pallas_call_p = hijax.HiPrimitive('pallas_call') pallas_call_p.multiple_results = True def _pallas_call_impl(*args, **params): # Call the lowering path @partial(api.jit, inline=True) def _jit_run(*args): return pallas_call_p.bind(*args, **params) with config.disable_jit(False): return _jit_run(*args) pallas_call_p.def_impl(_pallas_call_impl) def _pallas_call_abstract_eval( *avals, out_avals: tuple[jax_core.AbstractValue, ...], interpret, backend, input_output_aliases, grid_mapping, **params ): if isinstance(interpret, mosaic_tpu_interpret.InterpretParams): # Report effects that will be introduced when running/lowering # mosaic_tpu_interpret.mosaic_tpu_interpret.interpret_pallas_call . effs = mosaic_tpu_interpret.get_interpret_effects() elif getattr(params.get('compiler_params', None), 'has_side_effects', False): effs = jax_core.GenericEffect(pallas_call_p) else: effs = jax_core.no_effects # closed-over refs and dynamic grid bounds aren't reflected in # input_output_aliases, though they are present in `avals`, so split them off num_refs = sum(isinstance(a, state.AbstractRef) for a in avals) _, _, avals = split_list(avals, [num_refs, grid_mapping.num_dynamic_grid_bounds]) inout_aliases = dict(input_output_aliases) lin_avals = {i for i, a in enumerate(avals) if isinstance(a, state_types.AbstractLinVal)} if (missing := lin_avals - set(inout_aliases)): raise ValueError(f"input pinned buffers without input_output_aliases:" f"{missing}") outin_aliases = {out_idx: in_idx for in_idx, out_idx in inout_aliases.items()} out_avals = [jax_core.ShapedArray(a.shape, a.dtype, a.weak_type, sharding=a.sharding) if isinstance(a, pallas_core.ShapedArrayWithMemorySpace) else avals[outin_aliases[out_idx]] if out_idx in outin_aliases else a for out_idx, a in enumerate(out_avals)] # Make sure we don't return ShapedArrayWithMemorySpace to the outside world. return out_avals, effs pallas_call_p.def_effectful_abstract_eval(_pallas_call_abstract_eval) def _pallas_call_is_high(*_, jaxpr, **params): del params return jaxpr.is_high pallas_call_p.is_high = _pallas_call_is_high # type: ignore def _get_index_mapping(avals) -> dict[int, tuple[int, ...]]: indices = {} counter = 0 for i, in_aval in enumerate(avals): local_counter = [] for _ in range(len(in_aval.lo_ty())): local_counter.append(counter) counter += 1 indices[i] = tuple(local_counter) return indices def _pallas_call_to_lojax( *hi_args, jaxpr: jax_core.Jaxpr, input_output_aliases: tuple[tuple[int, int], ...], grid_mapping: GridMapping, mesh: pallas_core.Mesh | None, debug: bool, interpret: Any, compiler_params: Any, cost_estimate: CostEstimate | None, out_avals: tuple[jax_core.AbstractValue, ...], backend: Backend | None, metadata: FrozenDict[str, str] | None, name: str | None, ): if any(jax_core.get_aval(x).has_qdd for x in hi_args): raise NotImplementedError("pallas_call does not support QDD for inputs") if any(aval.has_qdd for aval in out_avals): raise NotImplementedError("pallas_call does not support QDD for outputs") closed_jaxpr = jax_core.ClosedJaxpr(jaxpr, ()) with grid_mapping.trace_env(): closed_lo_jaxpr = pe.lower_jaxpr(closed_jaxpr) assert not closed_lo_jaxpr.consts lo_jaxpr = closed_lo_jaxpr.jaxpr for block_mapping in grid_mapping.block_mappings: index_map_jaxpr = block_mapping.index_map_jaxpr if index_map_jaxpr.jaxpr.is_high: raise NotImplementedError( "pallas_call does not support hijax for index_map" ) avals = [jax_core.get_aval(a) for a in hi_args] lo_args = [lo_val for aval, x in zip(avals, hi_args) for lo_val in (aval.read_loval(x) if aval.has_qdd else aval.lower_val(x))] lo_out_avals = [ lo_aval for aval in out_avals for lo_aval in (aval.lo_ty() if aval.is_high else [aval]) ] lo_grid_mapping = grid_mapping.to_lojax() in_avals = [v.aval for v in lo_jaxpr.invars] scalar_prefetch_avals = in_avals[lo_grid_mapping.slice_index_ops] operand_avals = in_avals[lo_grid_mapping.slice_block_ops] scratch_avals = in_avals[lo_grid_mapping.slice_scratch_ops] # Some basic checks assert len(scalar_prefetch_avals) + len(operand_avals) + len( scratch_avals ) == len(in_avals) assert len(lo_grid_mapping.block_mappings) + len( lo_grid_mapping.scratch_avals ) + lo_grid_mapping.num_index_operands == len(lo_jaxpr.invars), ( len(lo_grid_mapping.block_mappings), len(lo_grid_mapping.scratch_avals), lo_grid_mapping.num_index_operands, len(lo_jaxpr.invars), ) # We need to update the input/output aliases to be in terms of the # flattened lo inputs/outputs. # Get mappings from hi input/outputs to the tuple of lo inputs/outputs input_index_mapping = _get_index_mapping(avals) output_index_mapping = _get_index_mapping(out_avals) new_input_output_aliases = [] # Alias lo inputs to lo outputs for i, o in input_output_aliases: assert i in input_index_mapping assert o in output_index_mapping for i_lo, o_lo in zip(input_index_mapping[i], output_index_mapping[o]): new_input_output_aliases.append((i_lo, o_lo)) lo_outs = pallas_call_p.bind( *lo_args, jaxpr=lo_jaxpr, grid_mapping=lo_grid_mapping, mesh=mesh, cost_estimate=cost_estimate, backend=backend, metadata=metadata, compiler_params=compiler_params, debug=debug, interpret=interpret, input_output_aliases=tuple(new_input_output_aliases), out_avals=tuple(lo_out_avals), name=name, ) return pe.raise_lo_outs(out_avals, lo_outs) pallas_call_p.to_lojax = _pallas_call_to_lojax # type: ignore def _pallas_call_jvp_rule( primals, tangents, *, jaxpr: jax_core.Jaxpr, input_output_aliases: tuple[tuple[int, int], ...], grid_mapping: GridMapping, mesh: pallas_core.Mesh | None, debug: bool, interpret: Any, compiler_params: Any, cost_estimate: CostEstimate | None, out_avals: tuple[jax_core.AbstractValue, ...], backend: Backend | None, metadata: FrozenDict[str, str] | None, name: str | None, ): debug_info = jaxpr.debug_info if grid_mapping.num_dynamic_grid_bounds: raise NotImplementedError("interpret with dynamic grid bounds unsupported") if grid_mapping.num_index_operands: raise NotImplementedError if input_output_aliases: raise NotImplementedError("JVP with aliasing not supported.") if mesh is not None: raise NotImplementedError("pallas_call with a mesh does not support JVP") nonzero_tangents = [not isinstance(t, ad_util.Zero) for t in tangents] tangents = [t for t in tangents if type(t) is not ad_util.Zero] nonzero_tangents_with_outputs = nonzero_tangents + [True] * grid_mapping.num_outputs closed_jaxpr = jax_core.ClosedJaxpr(jaxpr, ()) jvp_jaxpr_, _ = ad.jvp_jaxpr(closed_jaxpr, nonzero_tangents_with_outputs, []) jvp_jaxpr, () = jvp_jaxpr_.jaxpr, jvp_jaxpr_.consts # TODO consts # `pallas_call` takes in inputs and returns outputs but its jaxpr *does not*. # `pallas_call` takes in a stateful jaxpr, meaning the jaxpr accepts input # `Ref`s that are read from followed by output `Ref`s that are written to. # This means that when we do `jvp_jaxpr` on the `jaxpr`, we get out a new # jaxpr that has tangents following primals. In order for this jaxpr to be # compatible w/ `pallas_call` (inputs then outputs), we need to shuffle around # the jaxpr's invars. primal_refs, primal_out_refs, tangent_refs, tangent_out_refs = split_list( jvp_jaxpr.invars, [len(primals), grid_mapping.num_outputs, len(tangents)] ) invars = (*primal_refs, *tangent_refs, *primal_out_refs, *tangent_out_refs) effs = [] for eff in jvp_jaxpr.effects: if isinstance(eff, effects.JaxprInputEffect): eff = eff.replace( input_index=invars.index(jvp_jaxpr.invars[eff.input_index]) ) effs.append(eff) jvp_jaxpr = jvp_jaxpr.replace(invars=invars, effects=effs) if debug: print(f"\nThe jaxpr for the jvp of pallas_call {debug_info.func_src_info}:") print(jvp_jaxpr) in_bms, out_bms = split_list(grid_mapping.block_mappings, [len(primals)]) jvp_bms = (*in_bms, *in_bms, *out_bms, *out_bms) jvp_grid_mapping = grid_mapping.replace( block_mappings=jvp_bms, num_inputs=grid_mapping.num_inputs * 2, num_outputs=grid_mapping.num_outputs * 2, ) if cost_estimate is not None: jvp_cost_estimate = CostEstimate( flops=2 * cost_estimate.flops, bytes_accessed=2 * cost_estimate.bytes_accessed, transcendentals=2 * cost_estimate.transcendentals, ) else: jvp_cost_estimate = None out_flat = pallas_call_p.bind( *primals, *tangents, jaxpr=jvp_jaxpr, grid_mapping=jvp_grid_mapping, mesh=mesh, interpret=interpret, debug=debug, input_output_aliases=(), compiler_params=compiler_params, cost_estimate=jvp_cost_estimate, out_avals=(*out_avals, *out_avals), backend=backend, metadata=metadata, name=name, ) out_primals, out_tangents = split_list(out_flat, [len(out_flat) // 2]) return out_primals, out_tangents ad.primitive_jvps[pallas_call_p] = _pallas_call_jvp_rule def _batch_block_mapping( grid_mapping: GridMapping, axis_size: int, aval: jax_core.ShapedArray, dim: int | batching.NotMapped, block_mapping: BlockMapping, ) -> BlockMapping: def _block_map_function(new_idx, *args): drop_last_args = args indices = jax_core.eval_jaxpr( block_mapping.index_map_jaxpr.jaxpr, block_mapping.index_map_jaxpr.consts, *drop_last_args, ) unflat_indices = tree_util.tree_unflatten( block_mapping.index_map_out_tree, indices) if not isinstance(unflat_indices, tuple): unflat_indices = (unflat_indices,) unflat_indices = list(unflat_indices) if dim is not batching.not_mapped: unflat_indices.insert(dim, new_idx) return tuple(unflat_indices) idx_avals = [pallas_core.index_map_grid_aval, *block_mapping.index_map_jaxpr.in_avals] block_mapping_flat_fn, out_tree_thunk = api_util.flatten_fun_nokwargs( lu.wrap_init(_block_map_function, debug_info=block_mapping.index_map_jaxpr.jaxpr.debug_info.with_unknown_names()), tree_util.tree_structure(idx_avals)) with grid_mapping.trace_env(): block_mapping_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic( block_mapping_flat_fn, idx_avals) new_index_map_out_tree = out_tree_thunk() shape = block_mapping.block_shape if dim is batching.not_mapped: new_block_shape = shape new_array_aval = block_mapping.array_aval else: new_block_shape = tuple_insert(shape, dim, pallas_core.squeezed) array_shape = block_mapping.array_aval.shape array_shape = tuple_insert(array_shape, dim, axis_size) new_array_aval = jax_core.ShapedArray( array_shape, block_mapping.array_aval.dtype ) jaxpr = jax_core.ClosedJaxpr(block_mapping_jaxpr, consts) return block_mapping.replace(block_shape=new_block_shape, array_aval=new_array_aval, index_map_jaxpr=jaxpr, index_map_out_tree=new_index_map_out_tree) def _broadcast_input_output_aliases( args: Sequence[jax_typing.Array], dims: Sequence[int | batching.NotMapped], *, input_output_aliases: tuple[tuple[int, int], ...], axis_size: int, ) -> tuple[tuple[jax_typing.Array, ...], tuple[int | batching.NotMapped, ...]]: """Broadcast input/output operands. When we have input/output aliasing, since the output will be mapped, we need to make sure to broadcast the input across that dimension if it is not mapped. If the input is mapped, but on a different axis, we transpose the input to match the output. """ args_ = list(args) dims_ = list(dims) for input_index, _ in input_output_aliases: dim = dims_[input_index] dims_[input_index] = 0 if dim is batching.not_mapped: args_[input_index] = batching.broadcast( args_[input_index], axis_size, 0, None) elif dim != 0: # TODO(cjfj): Change output batching axis instead? args_[input_index] = jnp.moveaxis(args[input_index], dim, 0) return tuple(args_), tuple(dims_) def _batch_with_explicit_loop( args: Sequence[jax_typing.Array], dims: Sequence[int | batching.NotMapped], *, jaxpr: jax_core.Jaxpr, grid_mapping: GridMapping, mesh: pallas_core.Mesh | None, input_output_aliases: tuple[tuple[int, int], ...], debug: bool, interpret: Any, compiler_params: Any, cost_estimate: CostEstimate | None, out_avals: tuple[jax_core.AbstractValue, ...], backend: Backend | None, metadata: FrozenDict[str, str] | None, name: str | None, ): """Batch the pallas_call by calling it in loop over the batch size. This function provides a fallback implementation of batching a pallas_call for the cases in which adding a batch dimension to the pallas grid is not supported. This is currently the case when the batched dimension corresponds to a dynamic axis or a scalar prefetch argument. This implementation builds a HLO loop that dynamic_slices the inputs according to the current iteration index and dynamic_updates an (initially empty) output allocation. """ if not dims: raise NotImplementedError("vmapping pallas_call with no arguments.") (axis_size,) = { arg.shape[dim] for arg, dim in zip(args, dims) if dim is not batching.not_mapped } args, dims = _broadcast_input_output_aliases( args, dims, input_output_aliases=input_output_aliases, axis_size=axis_size, ) # The output arrays are completely overwritten, so we can just initialize # empty arrays. initial_state = [ jnp.empty(tuple_insert(bm.array_aval.shape, 0, axis_size), dtype=bm.array_aval.dtype) for bm in grid_mapping.block_mappings_output ] def body(batch_index: jax_typing.Array, state: list[jax_typing.Array]) -> list[jax_typing.Array]: batch_args = [] for arg, dim in zip(args, dims): # If the argument is mapped, extract a slice of size 1 in the mapped # dimension at the current index. if dim is batching.not_mapped: batch_args.append(arg) else: batch_args.append( jnp.squeeze( lax.dynamic_slice_in_dim( operand=arg, start_index=batch_index, slice_size=1, axis=dim, ), axis=dim, ) ) batch_out = pallas_call_p.bind( *batch_args, jaxpr=jaxpr, grid_mapping=grid_mapping, mesh=mesh, input_output_aliases=input_output_aliases, debug=debug, interpret=interpret, compiler_params=compiler_params, cost_estimate=cost_estimate, out_avals=out_avals, backend=backend, metadata=metadata, name=name, ) for i, batch_out_array in enumerate(batch_out): state[i] = lax.dynamic_update_index_in_dim( state[i], batch_out_array, batch_index, axis=0, ) return state result = lax.fori_loop(0, axis_size, body, initial_state, unroll=False) return result, (0,) * len(result) def _pallas_call_batching_rule( args, dims, *, jaxpr: jax_core.Jaxpr, grid_mapping: GridMapping, mesh: pallas_core.Mesh | None, input_output_aliases: tuple[tuple[int, int], ...], debug: bool, interpret: Any, compiler_params: Any, cost_estimate: CostEstimate | None, out_avals: tuple[jax_core.AbstractValue, ...], backend: Backend | None, metadata: FrozenDict[str, str] | None = None, name: str | None = None, ): if mesh is not None: raise NotImplementedError( "pallas_call with a mesh does not support batching" ) def _maybe_squeeze_out_bdim( x: jax_typing.Array, bdim: int | batching.NotMapped ) -> jax_typing.Array: if bdim is batching.not_mapped: return x return jnp.squeeze(x, axis=bdim) axis_size, = {x.shape[d] for i, (x, d) in enumerate(zip(args, dims)) if d is not batching.not_mapped} if axis_size == 1: # Why are we even vmapping? args = map(_maybe_squeeze_out_bdim, args, dims) out = pallas_call_p.bind( *args, jaxpr=jaxpr, grid_mapping=grid_mapping, mesh=mesh, input_output_aliases=input_output_aliases, debug=debug, interpret=interpret, compiler_params=compiler_params, cost_estimate=cost_estimate, out_avals=out_avals, backend=backend, metadata=metadata, name=name, ) return [jnp.expand_dims(x, 0) for x in out], (0,) * len(out) # The first num_dynamic_grid_bounds arguments are size-1 arrays that store # the size of the dynamic bounds. dynamic_grid_args, args = split_list( args, [grid_mapping.num_dynamic_grid_bounds] ) dynamic_grid_dims, dims = split_list( dims, [grid_mapping.num_dynamic_grid_bounds] ) if all( bdim is batching.not_mapped or arg.shape[bdim] == 1 for arg, bdim in zip(dynamic_grid_args, dynamic_grid_dims) ): dynamic_grid_args = safe_map( _maybe_squeeze_out_bdim, dynamic_grid_args, dynamic_grid_dims ) elif any(bdim is not batching.not_mapped for bdim in dynamic_grid_dims): # TODO(amagni, sharadmv): Explore possibility of batching dynamic grid # bounds. return _batch_with_explicit_loop( args=dynamic_grid_args + args, dims=dynamic_grid_dims + dims, jaxpr=jaxpr, grid_mapping=grid_mapping, mesh=mesh, input_output_aliases=input_output_aliases, debug=debug, interpret=interpret, compiler_params=compiler_params, cost_estimate=cost_estimate, out_avals=out_avals, backend=backend, metadata=metadata, name=name, ) else: pass # No dynamic grid dimensions del dynamic_grid_dims if grid_mapping.num_index_operands: scalar_args, args = split_list(args, [grid_mapping.num_index_operands]) scalar_bdims, bdims = split_list(dims, [grid_mapping.num_index_operands]) # Ordinarily, adding support for scalar prefetch in vmap would involve # modifying the block specs in a nontrivial way. However, if we are only # vmapping over 1-sized dimensions, we can just get rid of the dimensions # and pretend we were never vmapped over them at all. if all( bdim is batching.not_mapped or arg.shape[bdim] == 1 for arg, bdim in zip(scalar_args, scalar_bdims) ): scalar_args = safe_map(_maybe_squeeze_out_bdim, scalar_args, scalar_bdims) scalar_bdims = [batching.not_mapped] * len(scalar_args) args = (*scalar_args, *args) dims = (*scalar_bdims, *bdims) else: # TODO(amagni,sharadmv,apaszke): enable efficient batching over # prefetched scalar args. return _batch_with_explicit_loop( args=scalar_args + args, dims=scalar_bdims + bdims, jaxpr=jaxpr, grid_mapping=grid_mapping, mesh=mesh, input_output_aliases=input_output_aliases, debug=debug, interpret=interpret, compiler_params=compiler_params, cost_estimate=cost_estimate, out_avals=out_avals, backend=backend, metadata=metadata, name=name, ) if not dims: raise NotImplementedError("vmapping pallas_call with no arguments.") block_mappings = grid_mapping.block_mappings avals = [v.aval for v in jaxpr.invars] # How should we pick output dimensions? This actually matters because XLA # can't optimize our pallas kernels, and this layout impacts performance. For # now, because `vmap` doesn't really offer a way of inferring good output # dimensions. For now, we just use 0. # TODO(sharadmv): explore inferring better output dimensions via a heuristic # TODO(sharadmv): explore a long term solution to output dim inference args, dims = _broadcast_input_output_aliases( args, dims, input_output_aliases=input_output_aliases, axis_size=axis_size ) all_dims = list(dims) + [0] * grid_mapping.num_outputs num_index_operands = grid_mapping.num_index_operands num_scratch_operands = grid_mapping.num_scratch_operands # Only add a batch dimension for the avals that actually have a grid mapping. # This excludes scalar prefetch inputs (the first in the list) and scratch # operands (the last in the list). avals_to_batch = avals[num_index_operands:(len(avals) - num_scratch_operands)] batched_block_mappings = map( partial( _batch_block_mapping, grid_mapping, axis_size, ), avals_to_batch, all_dims[num_index_operands:], block_mappings, ) index_map_tree_args, index_map_tree_kwargs = grid_mapping.index_map_tree.unflatten( grid_mapping.index_map_avals) assert not index_map_tree_kwargs batched_index_map_args = (pallas_core.index_map_grid_aval,) + index_map_tree_args batched_index_map_avals, batched_index_map_tree = tree_util.tree_flatten( (batched_index_map_args, {})) batched_grid_mapping = grid_mapping.replace( grid=(axis_size, *grid_mapping.grid), block_mappings=tuple(batched_block_mappings), index_map_avals=tuple(batched_index_map_avals), index_map_tree=batched_index_map_tree, num_index_operands=num_index_operands, vmapped_dims=(0,) + tuple(a + 1 for a in grid_mapping.vmapped_dims), ) # Avoid scaling the cost estimate by the batch size if the batch size is a # dynamic shape (DimExpr). # https://docs.jax.dev/en/latest/export/shape_poly.html#computing-with-dimension-variables if cost_estimate is not None and isinstance(axis_size, int): batched_cost_estimate = CostEstimate( flops=cost_estimate.flops * axis_size, bytes_accessed=cost_estimate.bytes_accessed * axis_size, transcendentals=cost_estimate.transcendentals * axis_size, ) else: batched_cost_estimate = None assert all(isinstance(aval, jax_core.ShapedArray) for aval in out_avals) batched_out_avals = [] for aval in out_avals: sharding = aval.sharding.update(spec=tuple_insert(aval.sharding.spec, 0, None)) shape = tuple_insert(aval.shape, 0, axis_size) batched_out_avals.append(aval.update(shape=shape, sharding=sharding)) batched_out_avals = tuple(batched_out_avals) out = pallas_call_p.bind( *dynamic_grid_args, *args, jaxpr=jaxpr, grid_mapping=batched_grid_mapping, mesh=mesh, input_output_aliases=input_output_aliases, debug=debug, interpret=interpret, compiler_params=compiler_params, cost_estimate=batched_cost_estimate, out_avals=batched_out_avals, backend=backend, metadata=metadata, name=name, ) return out, (0,) * len(out) batching.primitive_batchers[pallas_call_p] = _pallas_call_batching_rule def checkify_pallas_kernel_body_jaxpr( body_jaxpr: jax_core.ClosedJaxpr, enabled_errors, error: checkify.Error, grid_mapping: GridMapping) -> tuple[ jax_core.ClosedJaxpr, tree_util.PyTreeDef, set[checkify.ErrorEffect]]: err_vals, err_tree = tree_util.tree_flatten(error) err_vals = map(jax_core.get_aval, err_vals) flat_err_and_in_vals = [*err_vals, *body_jaxpr.in_avals] with pallas_core.tracing_grid_env(grid_mapping.grid, ()): checked_jaxpr, out_tree, error_effects = checkify.jaxpr_to_checkify_jaxpr( body_jaxpr, enabled_errors, err_tree, *flat_err_and_in_vals) return checked_jaxpr, out_tree, error_effects def pallas_call_checkify_oob_grid(error: checkify.Error, enabled_errors, args: jax_core.Value, grid_mapping: GridMapping, input_output_aliases) -> checkify.Error: if checkify.OOBError not in enabled_errors: return error dynamic_grid_args, args = split_list( args, [grid_mapping.num_dynamic_grid_bounds] ) output_args = hlo_interpreter._initialize_output_vals(grid_mapping.block_mappings_output, args, input_output_aliases) scalars, input_args, _ = split_list( args, [grid_mapping.num_index_operands, grid_mapping.num_inputs], ) dynamic_grid_args_iter = iter(dynamic_grid_args) grid = tuple( a if a is not pallas_core.dynamic_grid_dim else next(dynamic_grid_args_iter) for a in grid_mapping.grid ) grid_start_indices = (jnp.int32(0),) * len(grid) if grid: num_iterations = reduce(jnp.multiply, grid) # type: ignore[arg-type] else: # Base case is always one iteration when grid is () num_iterations = 1 is_indexing_dim = [ tuple(isinstance(b, pallas_core.Squeezed) for b in bm.block_shape) for bm in grid_mapping.block_mappings ] block_shapes = [ pallas_core._get_block_shape(bm.block_shape) for bm in grid_mapping.block_mappings ] # The scan carry: (i, loop_idx, *consts, *ins, *outs, *scratch) # i:int32 is the iteration index # loop_idx: tuple[int32] are the program ids for each grid axis def cond(carry): i, *_ = carry return i < num_iterations def body(carry): i, loop_idx, blocks = carry if grid_mapping.local_grid_env is not None: local_grid_env = grid_mapping.local_grid_env(loop_idx, grid) else: local_grid_env = tuple( pallas_core.GridAxis(idx, b) for dim, (idx, b) in enumerate(zip(loop_idx, grid)) if dim not in grid_mapping.vmapped_dims ) with pallas_core.grid_env(local_grid_env): start_indices = [ None if bm is None else bm.compute_start_indices_interpret(loop_idx, *scalars) for bm in grid_mapping.block_mappings] # We perform a dynamic slice on the i/o blocks, which will be checked by # checkify for OOB accesses. blocks = map(hlo_interpreter._dynamic_slice, start_indices, block_shapes, [*input_args, *output_args], is_indexing_dim) return (i + 1, hlo_interpreter._get_next_indices(grid, loop_idx), blocks) def f(_): return lax.while_loop( cond, body, (jnp.int32(0), grid_start_indices, [jnp.zeros(shape) for shape in block_shapes]) ) flat_args, jaxpr_in_tree = tree_util.tree_flatten((jnp.int32(0),)) wrapped_loop, _ = api_util.flatten_fun_nokwargs( lu.wrap_init(f, debug_info=api_util.debug_info("checkify oob_grid_access", f, (0,), {})), jaxpr_in_tree) with pallas_core.tracing_grid_env(grid_mapping.grid, ()): avals_in = map(jax_core.get_aval, flat_args) traced_loop, _, consts = pe.trace_to_jaxpr_dynamic( wrapped_loop, list(avals_in)) traced_loop = jax_core.ClosedJaxpr(traced_loop, consts) out_error, _ = checkify.checkify_jaxpr( traced_loop, checkify.index_checks, error, flat_args) return out_error def pallas_call_checkify_rule(error: checkify.Error, enabled_errors, *args: jax_core.Value, jaxpr: jax_core.Jaxpr, interpret: Any, input_output_aliases: tuple[tuple[int, int], ...], grid_mapping: GridMapping, out_avals: tuple[jax_core.AbstractValue, ...], **kwargs): # Check for OOB accesses in the grid. error = pallas_call_checkify_oob_grid(error, enabled_errors, args, grid_mapping, input_output_aliases) # We implement the checkify rule in 4 steps: # 1) First, trace the kernel body to get the expected error shapes. # 2) Checkify the kernel body to obtain a jaxpr with errors as inputs # and outputs. # 3) Create a new kernel which stores the errors in output memrefs instead of # returning them, since pallas kernels do not return outputs. # 4) Create block specs for the error state and call pallas_call with # the new kernel. dynamic_grid_bounds, scalars, args = split_list( args, [grid_mapping.num_dynamic_grid_bounds, grid_mapping.num_index_operands] ) num_scalars = len(scalars) num_kernel_inputs = len(args) num_kernel_outputs = grid_mapping.num_outputs # Trace the jaxpr to get an initial error value so the kernel jaxpr has all of # the required inputs. closed_jaxpr = pe.close_jaxpr(jaxpr) _jaxpr, _, error_effects = checkify_pallas_kernel_body_jaxpr( closed_jaxpr, enabled_errors, error, grid_mapping) error = error._add_placeholder_effects(error_effects) err_vals, err_in_tree = tree_util.tree_flatten(error) shaped_err_avals = map(jax_core.get_aval, err_vals) # Trace the kernel jaxpr to get a checkified jaxpr. This jaxpr will have # all enabled errors removed, but have the error as inputs and return values. input_avals = [v.aval for v in jaxpr.invars] num_err_vals = len(err_vals) shaped_input_avals = tuple(input_avals) checkify_in_avals = [*shaped_err_avals, *shaped_input_avals] closed_kernel_jaxpr = pe.close_jaxpr(jaxpr) with pallas_core.tracing_grid_env(grid_mapping.grid, ()): checked_jaxpr, error_out_tree, _ = checkify.jaxpr_to_checkify_jaxpr( closed_kernel_jaxpr, enabled_errors, err_in_tree, *checkify_in_avals) # Create a new kernel to remove the error as an return value and instead # write them to a memref. This is because pallas kernels are expected # to have no return values but instead write their outputs to a ref. def checked_kernel_fn(*args): (scalars, in_error_refs, inputs, out_error_refs, outputs, scratch ) = split_list( args, [num_scalars, num_err_vals, num_kernel_inputs, num_err_vals, num_kernel_outputs]) # TODO(b/350593266): Remove zero-indexing once we support ()-shaped scalars. input_error_vals = [err_ref[0, 0] for err_ref in in_error_refs] # We need to re-order the inputs here. A checkified jaxpr always expects # errors before other arguments. jaxpr_args = [*input_error_vals, *scalars, *inputs, *outputs, *scratch] assert len(checked_jaxpr.jaxpr.invars) == len(jaxpr_args) result_flat = jax_core.eval_jaxpr( checked_jaxpr.jaxpr, checked_jaxpr.consts, *jaxpr_args) output_errors, _ = split_list(result_flat, [num_err_vals]) # Store new errors back in the error refs. for in_ref, out_ref, error in zip( in_error_refs, out_error_refs, output_errors): in_ref[0, 0] = error out_ref[0, 0] = error return [] # Trace the new checked_kernel_fn with Memref inputs so that # we can replace the old kernel jaxpr with the new checked jaxpr in # pallas_call. # ensure_2d_shape is only necessary because pallas does not support # ()-shaped Memrefs. # TODO(b/350593266): Remove once we support ()-shaped scalars. def _ensure_2d_error_shape(arg): if isinstance(arg, jax_core.ShapedArray): dtype = arg.dtype return jax_core.ShapedArray((1, 1) + arg.shape, dtype=dtype, weak_type=arg.weak_type) elif isinstance(arg, jax_typing.Array): return jnp.reshape(arg, (1, 1) + arg.shape) else: return jnp.array([[arg]]) shaped_err_avals = map(_ensure_2d_error_shape, shaped_err_avals) err_vals = map(_ensure_2d_error_shape, err_vals) error_memref_aval = [state.AbstractRef( err_val, pallas_core.MemorySpace.ERROR) for err_val in shaped_err_avals] shaped_scalar_avals, input_aval, output_aval, scratch_aval = split_list( shaped_input_avals, [num_scalars, num_kernel_inputs, num_kernel_outputs]) retrace_in_avals = [*shaped_scalar_avals, *error_memref_aval, *input_aval, *error_memref_aval, *output_aval, *scratch_aval] jaxpr_flat_avals, jaxpr_in_tree = tree_util.tree_flatten(retrace_in_avals) debug_info = api_util.debug_info("checkify_pallas", checked_kernel_fn, retrace_in_avals, {}) wrapped_kernel_with_err, out_tree_thunk = api_util.flatten_fun_nokwargs( lu.wrap_init(checked_kernel_fn, debug_info=debug_info), jaxpr_in_tree) with pallas_core.tracing_grid_env(grid_mapping.grid, ()): final_jaxpr, _, _ = pe.trace_to_jaxpr_dynamic( wrapped_kernel_with_err, jaxpr_flat_avals) # Prepare pallas_call inputs. We need to create new block specs # for the new error inputs and outputs. error_block_specs = [pallas_core.BlockSpec(None, None)] * len(shaped_err_avals) error_paths, _ = unzip2(tree_util.tree_flatten_with_path(error_block_specs)[0]) error_origins = tuple(f"errors[{tree_util.keystr(p)}" for p in error_paths) error_block_mappings = map( partial( pallas_core._convert_block_spec_to_block_mapping, index_map_avals=grid_mapping.index_map_avals, index_map_tree=grid_mapping.index_map_tree, grid=grid_mapping.grid, vmapped_dims=grid_mapping.vmapped_dims, debug=True, ), error_block_specs, error_origins, shaped_err_avals, ) input_block_mappings, output_block_mappings = split_list( grid_mapping.block_mappings, [num_kernel_inputs,]) grid_mapping_with_error = grid_mapping.replace( block_mappings=(*error_block_mappings, *input_block_mappings, *error_block_mappings, *output_block_mappings), num_inputs=grid_mapping.num_inputs + len(error_block_mappings), num_outputs=grid_mapping.num_outputs + len(error_block_mappings) ) # Bump all input_output_aliases by num_err_vals to make room for error # TODO(justinfu): Don't bump scalars here. input_output_aliases = tuple( (i+num_err_vals, o+num_err_vals) for (i, o) in input_output_aliases) input_output_aliases_with_error = tuple( (i+num_scalars, i) for i in range(num_err_vals)) + input_output_aliases new_vals_in = [*scalars, *err_vals, *args] new_out_avals = (*shaped_err_avals, *out_avals) result = pallas_call_p.bind(*dynamic_grid_bounds, *new_vals_in, jaxpr=final_jaxpr, interpret=interpret, grid_mapping=grid_mapping_with_error, input_output_aliases=input_output_aliases_with_error, out_avals=new_out_avals, **kwargs) errors, results = split_list(result, [num_err_vals]) # TODO(b/350593266): Remove line below once we support ()-shaped scalars. errors = [err_val[0, 0] for err_val in errors] new_error, _ = tree_util.tree_unflatten(error_out_tree, errors) return new_error, results checkify.error_checks[pallas_call_p] = pallas_call_checkify_rule def _trace_kernel_to_jaxpr( fun: Callable, debug_info: jax_core.DebugInfo, grid_mapping: GridMapping, kernel_avals: tuple[pallas_core.AbstractMemRef, ...], kernel_in_tree: tree_util.PyTreeDef, kernel_in_transforms: tuple[tuple[pallas_core.Transform, ...], ...], indexer: bool = False, ) -> tuple[jax_core.Jaxpr, tuple[jax_typing.Array, ...]]: wrapped_kernel_fun, out_tree_thunk = api_util.flatten_fun_nokwargs( lu.wrap_init(fun, debug_info=debug_info), kernel_in_tree) wrapped_kernel_fun = primitives.wrap_with_transforms( wrapped_kernel_fun, kernel_in_transforms ) with grid_mapping.trace_env(), config._check_vma(False): with config.mutable_array_checks(False): jaxpr, _, consts = pe.trace_to_jaxpr_dynamic( wrapped_kernel_fun, kernel_avals) if consts: consts_avals = [ aval for c in consts if not isinstance(aval := jax_core.get_aval(c), state.AbstractRef) ] if consts_avals: ctx = jax_core.JaxprPpContext() pp_consts_avals = ", ".join( jax_core.pp_aval(aval, ctx) for aval in consts_avals ) raise ValueError( "The kernel function in the pallas_call" f" {debug_info.func_src_info} captures constants" f" [{pp_consts_avals}]. You should pass them as inputs." ) kernel_out_tree = out_tree_thunk() if not indexer and kernel_out_tree != tree_util.tree_structure(None): raise ValueError( f"The kernel function in the pallas_call {debug_info.func_src_info} " f"should return None. It returns a PyTree: {kernel_out_tree}") return jaxpr, tuple(consts) _PALLAS_USE_MOSAIC_GPU = config.bool_state( "jax_pallas_use_mosaic_gpu", default=config.bool_env("JAX_PALLAS_USE_MOSAIC_GPU", False), help=( "If True, lower Pallas kernels to the experimental Mosaic GPU" " dialect, instead of Triton IR." ), ) def _unsupported_lowering_error(platform: str) -> Exception: return ValueError( f"Cannot lower pallas_call on platform: {platform}. To use Pallas on GPU," " install jaxlib GPU 0.4.24 or newer. To use Pallas on TPU, install" " jaxlib TPU and libtpu. See" " https://docs.jax.dev/en/latest/installation.html." ) def _pallas_call_lowering( ctx: mlir.LoweringRuleContext, *in_nodes, interpret: Any, backend: Backend | None, **params, ): if params['jaxpr'].constvars: raise ValueError('Cannot lower a pallas_call with constants.') if interpret: if isinstance(interpret, mosaic_tpu_interpret.InterpretParams): impl = partial(mosaic_tpu_interpret.interpret_pallas_call, interpret_params=interpret, **params) else: impl = partial(hlo_interpreter.pallas_call_hlo_interpret, backend=backend, **params) return mlir.lower_fun(impl, multiple_results=True)(ctx, *in_nodes) def cpu_lowering(ctx: mlir.LoweringRuleContext, *in_nodes: mlir.ir.Value | Sequence[mlir.ir.Value], **params): raise ValueError("Only interpret mode is supported on CPU backend.") def tpu_lowering(ctx: mlir.LoweringRuleContext, *in_nodes: mlir.ir.Value | Sequence[mlir.ir.Value], **params): if backend and backend != "mosaic_tpu": raise ValueError("Only mosaic backend supported for TPU") if mosaic_tpu_backend is None: raise _unsupported_lowering_error("tpu") return mosaic_tpu_backend.pallas_call_tpu_lowering_rule( ctx, *in_nodes, **params ) def gpu_lowering(ctx: mlir.LoweringRuleContext, *in_nodes: mlir.ir.Value | Sequence[mlir.ir.Value], **params): try: match backend: case "mosaic_gpu": from jax._src.pallas.mosaic_gpu import pallas_call_registration case "triton": from jax._src.pallas.triton import pallas_call_registration # type: ignore case None: if _PALLAS_USE_MOSAIC_GPU.value: from jax._src.pallas.mosaic_gpu import pallas_call_registration else: from jax._src.pallas.triton import pallas_call_registration # type: ignore case _: raise ValueError(f"Unsupported backend: {backend}") except ImportError as e: raise _unsupported_lowering_error("gpu") return pallas_call_registration.pallas_call_lowering( ctx, *in_nodes, **params ) return mlir.lower_per_platform(ctx, "pallas_call", dict(cpu=cpu_lowering, tpu=tpu_lowering, cuda=gpu_lowering, rocm=gpu_lowering), None, # default_rule effects.no_effects, *in_nodes, interpret=interpret, **params) mlir.register_lowering(pallas_call_p, _pallas_call_lowering) def _pallas_custom_str_eqn_compact( prim: jax_core.Primitive, params: dict[Any, Any] ) -> str: del prim, params # Hide most info from compact str representation return "pallas_call" jax_core.custom_str_eqn_compact_rules[pallas_call_p] = ( _pallas_custom_str_eqn_compact ) def _pallas_call_typecheck_rule(ctx_factory, *in_atoms, grid_mapping, **params): in_avals = [x.aval for x in in_atoms] with grid_mapping.trace_env(): return pallas_call_p.abstract_eval( *in_avals, grid_mapping=grid_mapping, **params ) jax_core.custom_typechecks[pallas_call_p] = _pallas_call_typecheck_rule def _convert_out_shape_to_aval(out_shape: Any) -> jax_core.AbstractValue: match out_shape: case jax_core.ShapeDtypeStruct(): if config._check_vma.value: if out_shape.vma is None: raise ValueError( "When `check_vma=True` on `jax.shard_map`, `vma` on" " `jax.ShapeDtypeStruct` must not be `None`. Please specify how the" " output should be varying across mesh axes using the `vma`" " argument of `jax.ShapeDtypeStruct` or set `check_vma=False` on" " `jax.shard_map`.") return jax_core.ShapedArray( shape=out_shape.shape, dtype=out_shape.dtype, sharding=jax_core.get_cur_mesh_sharding(), vma=out_shape.vma) return jax_core.ShapedArray(shape=out_shape.shape, dtype=out_shape.dtype, sharding=jax_core.get_cur_mesh_sharding()) case pallas_core.MemoryRef(): return out_shape.get_array_aval() case hijax.HiType(): return out_shape case _: if type(out_shape) in pallas_core._out_shape_to_aval_mapping: return pallas_core._out_shape_to_aval_mapping[type(out_shape)]( out_shape ) if not (hasattr(out_shape, "shape") and hasattr(out_shape, "dtype")): raise ValueError(f"Invalid out_shape type: {type(out_shape)}") return jax_core.ShapedArray(shape=out_shape.shape, dtype=out_shape.dtype) @state_discharge.register_discharge_rule(pallas_call_p) def _pallas_call_state_discharge_rule( avals_in, avals_out, *args, jaxpr: jax_core.Jaxpr, input_output_aliases: tuple[tuple[int, int], ...], grid_mapping: GridMapping, mesh: pallas_core.Mesh | None, debug: bool, interpret: Any, compiler_params: Any, cost_estimate: CostEstimate | None, out_avals: tuple[jax_core.AbstractValue, ...], backend: Backend | None, metadata: FrozenDict[str, str] | None, name: str | None, ): del avals_out assert all(isinstance(v.aval, state.AbstractRef) for v in jaxpr.constvars) num_refs = len(jaxpr.constvars) ref_avals, rest_in_avals = split_list(avals_in, [num_refs]) assert all(isinstance(ref_aval, state.AbstractRef) for ref_aval in ref_avals) ref_avals = [ state.AbstractRef( ref_aval.inner_aval, pallas_core.MemorySpace.ANY ) for ref_aval in ref_avals ] ref_block_specs = [ pallas_core.BlockSpec(memory_space=pallas_core.MemorySpace.ANY) ] * num_refs ref_block_mappings = [ block_spec.to_block_mapping( origin="", # TODO(sharadmv): enable origins for refs array_aval=ref_aval.inner_aval, # type: ignore[arg-type] index_map_avals=grid_mapping.index_map_avals, index_map_tree=grid_mapping.index_map_tree, grid=grid_mapping.grid, vmapped_dims=grid_mapping.vmapped_dims, debug=debug, ) for ref_aval, block_spec in zip(ref_avals, ref_block_specs) ] in_block_mappings, out_block_mappings = split_list( grid_mapping.block_mappings, [grid_mapping.num_inputs] ) new_block_mappings = ( *ref_block_mappings, *in_block_mappings, *ref_block_mappings, *out_block_mappings, ) new_grid_mapping = grid_mapping.replace( block_mappings=new_block_mappings, num_inputs=grid_mapping.num_inputs + num_refs, num_outputs=grid_mapping.num_outputs + num_refs) new_input_output_aliases = [ (i + grid_mapping.num_index_operands, i) for i in range(num_refs) ] for i, o in input_output_aliases: new_input_output_aliases.append((i + num_refs, o + num_refs)) ref_out_avals = [ref_aval.inner_aval for ref_aval in ref_avals] new_out_avals = (*ref_out_avals, *out_avals) ref_args, dynamic_grid_bounds, index_operands, rest_args = split_list( args, [ num_refs, grid_mapping.num_dynamic_grid_bounds, grid_mapping.num_index_operands, ], ) def _rewritten_body(*args): index_args, in_args, out_args, rest_args = split_list( args, [new_grid_mapping.num_index_operands, new_grid_mapping.num_inputs, new_grid_mapping.num_outputs]) ref_in_args, in_args = split_list(in_args, [num_refs]) ref_out_args, out_args = split_list(out_args, [num_refs]) # We don't care about ref_out_args because they are aliased to ref_in_args del ref_out_args jax_core.eval_jaxpr( jaxpr, ref_in_args, *index_args, *in_args, *out_args, *rest_args ) return [] index_map_avals, jaxpr_in_avals, jaxpr_out_avals, jaxpr_rest_avals = ( split_list( [v.aval for v in jaxpr.invars], [ grid_mapping.num_index_operands, grid_mapping.num_inputs, grid_mapping.num_outputs, ], ) ) new_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic( lu.wrap_init(_rewritten_body, debug_info=jaxpr.debug_info.with_unknown_names()), [ *index_map_avals, *ref_avals, *jaxpr_in_avals, *ref_avals, *jaxpr_out_avals, *jaxpr_rest_avals, ], ) out_flat = pallas_call_p.bind( *consts, *dynamic_grid_bounds, *index_operands, *ref_args, *rest_args, jaxpr=new_jaxpr, input_output_aliases=tuple(new_input_output_aliases), grid_mapping=new_grid_mapping, mesh=mesh, debug=debug, interpret=interpret, compiler_params=compiler_params, cost_estimate=cost_estimate, out_avals=new_out_avals, backend=backend, metadata=metadata, name=name, ) refs_out, rest = split_list(out_flat, [num_refs]) updated_vals_in = refs_out + [None] * len(rest_in_avals) return updated_vals_in, rest def pallas_call( kernel: Callable[..., None], out_shape: Any, *, grid_spec: pallas_core.GridSpec | None = None, grid: pallas_core.TupleGrid = (), in_specs: pallas_core.BlockSpecTree = no_block_spec, out_specs: pallas_core.BlockSpecTree = no_block_spec, scratch_shapes: pallas_core.ScratchShapeTree = (), input_output_aliases: Mapping[int, int] = {}, debug: bool = False, interpret: Any = False, name: str | None = None, compiler_params: ( Mapping[Backend, pallas_core.CompilerParams] | pallas_core.CompilerParams | None ) = None, cost_estimate: CostEstimate | None = None, backend: Backend | None = None, metadata: dict[str, str] | None = None, ) -> Callable[..., Any]: """Entry point for creating a Pallas kernel. In contrast to :func:`jax.experimental.pallas.kernel`, this entry point assumes that the kernel will be executed over a ``grid``. See `Pallas Quickstart `_. Args: kernel: the kernel function, that receives a Ref for each input and output. The shape of the Refs are given by the ``block_shape`` in the corresponding ``in_specs`` and ``out_specs``. out_shape: a PyTree of :class:`jax.ShapeDtypeStruct` describing the shape and dtypes of the outputs. grid_spec: An alternative way to specify ``grid``, ``in_specs``, ``out_specs`` and ``scratch_shapes``. If given, those other parameters must not be also given. grid: the iteration space, as a tuple of integers. The kernel is executed as many times as ``prod(grid)``. See details at :ref:`pallas_grid`. in_specs: a PyTree of :class:`jax.experimental.pallas.BlockSpec` with a structure matching that of the positional arguments. The default value for ``in_specs`` specifies the whole array for all inputs, e.g., as ``pl.BlockSpec(x.shape, lambda *indices: (0,) * x.ndim)``. See details at :ref:`pallas_blockspec`. out_specs: a PyTree of :class:`jax.experimental.pallas.BlockSpec` with a structure matching that of the outputs. The default value for ``out_specs`` specifies the whole array, e.g., as ``pl.BlockSpec(x.shape, lambda *indices: (0,) * x.ndim)``. See details at :ref:`pallas_blockspec`. scratch_shapes: a PyTree of backend-specific temporary objects required by the kernel, such as temporary buffers, synchronization primitives, etc. input_output_aliases: a dictionary mapping the index of some inputs to the index of the output that aliases them. These indices are in the flattened inputs and outputs (ignoring None values). debug: if True, Pallas prints various intermediate forms of the kernel as it is being processed. interpret: runs the ``pallas_call`` as a ``jax.jit`` of a scan over the grid whose body is the kernel lowered as a JAX function. This does not require a TPU or a GPU, and is the only way to run Pallas kernels on CPU. This is useful for debugging. name: if present, specifies the name to use for this kernel call in debugging and error messages. To this name we append the file and line where the kernel function is defined, .e.g: `{name} for kernel function {kernel_name} at {file}:{line}`. If missing, then we use `{kernel_name} at {file}:{line}`. compiler_params: Optional compiler parameters. The value should either be a backend-specific dataclass (:class:`jax.experimental.pallas.tpu.CompilerParams`, :class:`jax.experimental.pallas.triton.CompilerParams`, :class:`jax.experimental.pallas.mosaic_gpu.CompilerParams`) or a dict mapping backend name to the corresponding platform-specific dataclass. backend: Optional string literal one of ``"mosaic_tpu"``, ``"triton"`` or ``"mosaic_gpu"`` determining the backend to be used. None means let Pallas decide. metadata: Optional dictionary of information about the kernel that will be serialized as JSON in the HLO. Can be used for debugging and analysis. Returns: A function that can be called on a number of positional array arguments to invoke the Pallas kernel. """ if grid_spec is None: grid_spec = pallas_core.GridSpec(grid, in_specs, out_specs, scratch_shapes) else: if grid: raise ValueError( "If `grid_spec` is specified, then `grid` must " f"be `()`. It is {grid}") if in_specs is not no_block_spec: raise ValueError( "If `grid_spec` is specified, then `in_specs` must " f"be `no_block_spec`. It is {in_specs}") if out_specs is not no_block_spec: raise ValueError( "If `grid_spec` is specified, then `out_specs` must " f"be `no_block_spec`. It is {out_specs}") if scratch_shapes: raise ValueError( "If `grid_spec` is specified, then `scratch_shapes` must " f"be `()`. It is {scratch_shapes}") del grid, in_specs, out_specs # We can infer a backend from compiler_params if it is not specified. if backend is None and isinstance(compiler_params, pallas_core.CompilerParams): backend = compiler_params.BACKEND return _pallas_call( kernel, out_shape, grid_spec=grid_spec, input_output_aliases=input_output_aliases, debug=debug, interpret=interpret, name=name, compiler_params=compiler_params, cost_estimate=cost_estimate, backend=backend, metadata=metadata, ) def _normalize_compiler_params( compiler_params: Mapping[Backend, pallas_core.CompilerParams] | pallas_core.CompilerParams | None, ) -> Mapping[Backend, pallas_core.CompilerParams]: if compiler_params is None: return FrozenDict({}) if isinstance(compiler_params, CompilerParams): compiler_params = {compiler_params.BACKEND: compiler_params} assert isinstance(compiler_params, Mapping) for backend, params in compiler_params.items(): if backend not in ["mosaic_tpu", "mosaic_gpu", "triton"]: raise ValueError(f"Unknown backend in compiler_params: {backend}") if not isinstance(params, CompilerParams): raise ValueError( f"Unexpected compiler_params for backend {backend}: {params}" ) if params.BACKEND != backend: raise ValueError( f"Inconsistent backend in compiler_params: {params.BACKEND} !=" f" {backend}" ) if not isinstance(compiler_params, FrozenDict): compiler_params = FrozenDict(compiler_params) return compiler_params @partial(api_boundary, repro_api_name="jax.experimental.pallas.pallas_call") def _pallas_call( kernel: Callable[..., None], out_shape: Any, *, grid_spec: pallas_core.GridSpec, mesh: pallas_core.Mesh | None = None, input_output_aliases: Mapping[int, int] = {}, debug: bool = False, interpret: Any = False, name: str | None = None, compiler_params: ( Mapping[Backend, CompilerParams] | CompilerParams | None ) = None, cost_estimate: CostEstimate | None = None, backend: Backend | None = None, metadata: dict[str, str] | None = None, ): interpret = ( config.pallas_tpu_interpret_mode_context_manager.value or interpret) compiler_params = _normalize_compiler_params(compiler_params) if mesh is not None: if tuple(mesh.shape.values()) != grid_spec.grid: raise ValueError( f"Mesh shape {tuple(mesh.shape.values())} does not match grid " f"shape {grid_spec.grid}." ) if backend is not None: raise ValueError("If `mesh` is specified, then `backend` must be `None`.") backend = mesh.backend grid_spec, dynamic_grid_bounds = pallas_core.unzip_dynamic_grid_bounds(grid_spec) # TODO(necula): this canonicalization may be convenient for some usage # but it is lossy, because it prevents expressing functions that return # lists. if isinstance(out_shape, list): out_shape = tuple(out_shape) flat_out_shapes_with_paths, out_tree = tree_util.tree_flatten_with_path(out_shape) out_paths, flat_out_shapes = unzip2(flat_out_shapes_with_paths) @partial(api.jit, inline=True) def wrapped(*args): flat_args_with_paths, in_tree = tree_util.tree_flatten_with_path(args) in_paths, flat_args = unzip2(flat_args_with_paths) flat_in_avals = tuple(jax_core.get_aval(a) for a in flat_args) flat_out_avals = tuple(_convert_out_shape_to_aval(v) for v in flat_out_shapes) in_origins = tuple(f"args{tree_util.keystr(p)}" for p in in_paths) out_origins = tuple(f"outputs{tree_util.keystr(p)}" for p in out_paths) # TODO(necula): check that input_output_aliases is well-formed: no duplicates, etc. kernel_args, grid_mapping = pallas_core.get_grid_mapping( grid_spec, flat_in_avals, in_tree, in_origins, flat_out_avals, out_tree, out_origins, debug, ) flat_kernel_args, kernel_in_tree = tree_util.tree_flatten(kernel_args) flat_kernel_avals = tuple( x.ref if isinstance(x, state_types.TransformedRef) else x for x in flat_kernel_args ) if config._check_vma.value: flat_kernel_avals = tuple(a.update_vma(frozenset()) for a in flat_kernel_avals) # Note that only a subset of all transforms can be found here, and they are # never expected to contain any arrays. kernel_arg_transforms = tuple( x.transforms if isinstance(x, state_types.TransformedRef) else () for x in flat_kernel_args ) kernel_dbg = api_util.debug_info("pallas_call kernel", kernel, kernel_args, {}) if name is not None: kernel_dbg = kernel_dbg.replace_func_name(mlir.sanitize_name(name)) jaxpr, consts = _trace_kernel_to_jaxpr( kernel, kernel_dbg, grid_mapping, flat_kernel_avals, kernel_in_tree, kernel_arg_transforms) for i_idx, o_idx in input_output_aliases.items(): if i_idx not in range(len(flat_in_avals)): raise ValueError( f"input_output_aliases contains the mapping '{i_idx}:{o_idx}' with " f"input index {i_idx} outside the range " f"[0, {len(flat_in_avals)})") if o_idx not in range(len(flat_out_avals)): raise ValueError( f"input_output_aliases contains the mapping '{i_idx}:{o_idx}' with " f"output index {o_idx} outside the range " f"[0, {len(flat_out_avals)})") in_aval = flat_in_avals[i_idx] out_aval = flat_out_avals[o_idx] if in_aval.shape != out_aval.shape or in_aval.dtype != out_aval.dtype: raise ValueError( f"input_output_aliases contains the mapping '{i_idx}:{o_idx}' " f"referring to input{tree_util.keystr(in_paths[i_idx])} with " f"abstract value {in_aval} " f"and to output{tree_util.keystr(out_paths[o_idx])} with " f"a different abstract value {out_aval}.") index_args, rest_args = split_list(flat_args, [grid_mapping.num_index_operands]) ctx = ( api.named_scope(name) if name is not None else contextlib.nullcontext() ) with ctx: out_flat = pallas_call_p.bind( *consts, *dynamic_grid_bounds, *index_args, *rest_args, out_avals=flat_out_avals, jaxpr=jaxpr, debug=debug, interpret=interpret, grid_mapping=grid_mapping, mesh=mesh, input_output_aliases=tuple(input_output_aliases.items()), compiler_params=compiler_params, cost_estimate=cost_estimate, backend=backend, metadata=FrozenDict(metadata) if metadata is not None else None, name=name, ) out = tree_util.tree_unflatten(out_tree, out_flat) return out return wrapped def in_path_to_input_origin( in_path: tree_util.KeyPath, arg_names: tuple[str, ...] | None ) -> pallas_core.OriginStr: """Converts `args[k]` into `arg_k_name`.""" if arg_names is None: return f"args{tree_util.keystr(in_path)}" if len(in_path) == 0: return "args" arg_idx, *rest_path = in_path if isinstance(arg_idx, tree_util.SequenceKey) and arg_idx.idx < len( arg_names ): if arg_names[arg_idx.idx] is None: # TODO(necula): when is this needed? # Repro: pallas_test:test_with_input_output_aliasing return f"args{tree_util.keystr(in_path)}" return arg_names[arg_idx.idx] + tree_util.keystr(tuple(rest_path)) else: return f"args{tree_util.keystr(tuple(in_path))}" # We import the TPU backend at the top level because it defines flags. Note that # we can only do that at the bottom of this file, because it also depends on # this module already being initialized. try: from jax._src.pallas.mosaic import pallas_call_registration as mosaic_tpu_backend except ImportError: mosaic_tpu_backend = None # type: ignore try: from jax._src.pallas.mosaic.interpret import interpret_pallas_call as mosaic_tpu_interpret except ImportError: mosaic_tpu_interpret = types.SimpleNamespace( # type: ignore InterpretParams=types.new_class('_NoInstances', (enum.Enum,)), )