Tianqi Chen
1e1920bcbd
In the past we have been using `DataType` in PrimExpr.dtype field to check type information for PrimExpr while still having BaseExpr.ty for richer type information. DataType is also used both in runtime and compiler. This PR streamlines the boundary: - PrimExpr.ty now carries PrimType that replaces original use of `DataType` - Runtime use will now favor DLPack DLDataType, removing one layer of indirection. - Constants attributes where values are usually runtime values, will use `DLDataType` - DataType will be phased out after this PR We also brings up helper functions in PrimType, but also limits them to a more concise set so the functions do not grow with the data type codes in DLPack. This is a major refactor that changes the IR primitive. It helps to bring possible future benefits: - Unified type mechanism through Expr.ty - Possibility of carry future Type nodes Migration Guide: - Use `PrimType` when code reasons about compiler expression types, tensor element compiler types, or constructs a `PrimExpr`/compiler type. - Use existing source types such as `expr.ty()`, `ExprOp.expr_ty()`, or TE tensor element `dtype` where possible instead of rebuilding a type from dtype text. - Use raw `DLDataType` for runtime constants, ABI paths, dtype-valued attrs, and storage/runtime helper logic. - Prefer direct `PrimType` equality, `MatchesCode(...)`, `MatchesElementType(...)`, and `WithCode(...)` over local wrappers or string dtype checks. Performance: Using Object type instead of DLDataType would indeed bring some performance impact to the IR. We have done the following performance optimizations: - Make sure most of the outputs reuse one of the PrimType from inputs - Cache a thread local PrimType based on input so we don't repeatly realloc We did benchmarks show that rewrite simplify operation stays within +-10% overhead of original one. Which merits the refactor given the benefit the unfication brings
94 lines
3.9 KiB
Python
94 lines
3.9 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name, unused-argument, redefined-argument-from-local
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"""Dispatch sampling operators to platform dependent implementation."""
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from tvm import relax
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from tvm.ir import Op
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from tvm.ir.module import IRModule
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from tvm.ir.transform import PassContext, module_pass
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from tvm.relax import expr_functor
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from .utils import BackendDispatcher
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@expr_functor.mutator
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class SamplingDispatcher(BackendDispatcher):
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"""Dispatcher to dispatch sampling op."""
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def visit_call_(self, call: relax.Call) -> relax.Expr:
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if not isinstance(call.op, Op):
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return super().visit_call_(call)
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if call.op.name == "relax.multinomial_from_uniform":
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from tvm.relax.backend.gpu_generic import ( # pylint: disable=import-outside-toplevel
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generic_get_sample_index,
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gpu_multinomial_from_uniform,
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)
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prob, uniform_sample, sample_indices = call.args
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tgt = self._get_target(call.ty)
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dtype = call.attrs.dtype
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_, prob_dtype = self.get_shape_dtype(prob)
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sample_shape, sample_dtype = self.get_shape_dtype(uniform_sample)
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sample_indices_shape, sample_indices_dtype = self.get_shape_dtype(sample_indices)
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if len(sample_shape) != 2 or sample_shape[1] != 1:
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raise ValueError("uniform_sample should be a 2D tensor with shape (N, 1)")
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if len(sample_indices_shape) != 2 or sample_indices_shape[1] != 1:
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raise ValueError("sample_indices should be a 2D tensor with shape (N, 1)")
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if self.is_gpu_target(tgt):
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gv = self.builder_.add_func(
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gpu_multinomial_from_uniform(
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prob_dtype, sample_dtype, sample_indices_dtype, dtype
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),
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"gpu_multinomial_from_uniform",
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)
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return relax.call_tir(
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gv,
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[prob, uniform_sample, sample_indices],
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out_ty=call.ty,
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)
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else:
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cumsum_prob = relax.op.cumsum(prob, axis=1, dtype=prob_dtype.dtype, exclusive=False)
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gv = self.builder_.add_func(
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generic_get_sample_index(prob_dtype, sample_dtype, sample_indices_dtype, dtype),
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"get_sample_index",
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)
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return relax.call_tir(
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gv,
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[cumsum_prob, uniform_sample, sample_indices],
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out_ty=call.ty,
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)
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return super().visit_call_(call)
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@module_pass(opt_level=0, name="DispatchSampling")
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class DispatchSampling:
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"""Pass to dispatch scan and sort operators to platform dependent implementation."""
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def transform_module(self, mod: IRModule, ctx: PassContext) -> IRModule:
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sampling_dispatcher = SamplingDispatcher(mod)
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for gv, func in mod.functions_items():
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if isinstance(func, relax.Function):
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func = sampling_dispatcher.visit_expr(func)
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sampling_dispatcher.builder_.update_func(gv, func)
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return sampling_dispatcher.builder_.finalize()
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