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This commit integrates hipBLAS into TVM. The minimum ROCm version requirement is 6.0. Co-authored-by: Lesheng Jin <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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 | ||
| # | ||
| # http://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. | ||
| """External function interface to hipBLAS libraries.""" | ||
| import tvm | ||
| from tvm import te | ||
|
|
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|
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| def matmul(lhs, rhs, transa=False, transb=False, dtype=None): | ||
| """Create an extern op that compute matrix mult of A and rhs with cuBLAS | ||
| Parameters | ||
| ---------- | ||
| lhs : Tensor | ||
| The left matrix operand | ||
| rhs : Tensor | ||
| The right matrix operand | ||
| transa : bool | ||
| Whether transpose lhs | ||
| transb : bool | ||
| Whether transpose rhs | ||
| Returns | ||
| ------- | ||
| C : Tensor | ||
| The result tensor. | ||
| """ | ||
| n = lhs.shape[1] if transa else lhs.shape[0] | ||
| m = rhs.shape[0] if transb else rhs.shape[1] | ||
| dtype = dtype if dtype is not None else lhs.dtype | ||
| return te.extern( | ||
| (n, m), | ||
| [lhs, rhs], | ||
| lambda ins, outs: tvm.tir.call_packed( | ||
| "tvm.contrib.hipblas.matmul", ins[0], ins[1], outs[0], transa, transb | ||
| ), | ||
| dtype=dtype, | ||
| name="matmul_hipblas", | ||
| ) | ||
|
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| def batch_matmul(lhs, rhs, transa=False, transb=False, dtype=None): | ||
| """Create an extern op that compute batch matrix mult of A and rhs with cuBLAS | ||
| Parameters | ||
| ---------- | ||
| lhs : Tensor | ||
| The left matrix operand | ||
| rhs : Tensor | ||
| The right matrix operand | ||
| transa : bool | ||
| Whether transpose lhs | ||
| transb : bool | ||
| Whether transpose rhs | ||
| Returns | ||
| ------- | ||
| C : Tensor | ||
| The result tensor. | ||
| """ | ||
| b = lhs.shape[0] | ||
| n = lhs.shape[2] if transa else lhs.shape[1] | ||
| m = rhs.shape[1] if transb else rhs.shape[2] | ||
| dtype = dtype if dtype is not None else lhs.dtype | ||
| return te.extern( | ||
| (b, n, m), | ||
| [lhs, rhs], | ||
| lambda ins, outs: tvm.tir.call_packed( | ||
| "tvm.contrib.hipblas.batch_matmul", ins[0], ins[1], outs[0], transa, transb | ||
| ), | ||
| dtype=dtype, | ||
| name="batch_matmul_hipblas", | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,180 @@ | ||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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 | ||
| # | ||
| # http://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. | ||
|
|
||
| """Pattern table for hipblas backend""" | ||
| import operator | ||
| from functools import reduce | ||
|
|
||
| import tvm | ||
| from tvm.relax import transform | ||
| from tvm.relax.transform import PatternCheckContext | ||
|
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| from ..pattern_registry import get_patterns_with_prefix, register_patterns | ||
| from ..patterns import make_matmul_pattern | ||
| from ..utils import has_leaking_intermediate_variables | ||
|
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| def _is_supported_dtype(lhs_dtype, rhs_dtype, out_dtype): # pylint: disable=unused-argument | ||
| """Check if dtypes in the given workload are supported by hipblas BYOC.""" | ||
| if lhs_dtype == "e4m3_float8" and rhs_dtype == "e4m3_float8": | ||
| # The output cannot be 'e5m2_float8' if inputs are 'e4m3_float8' | ||
| # return out_dtype != "e5m2_float8" | ||
| return False | ||
| return (lhs_dtype == "float16" and rhs_dtype == "float16") or ( | ||
| lhs_dtype == "int8" and rhs_dtype == "int8" | ||
| ) | ||
|
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| def _check_matmul(context: PatternCheckContext) -> bool: | ||
| if has_leaking_intermediate_variables(context): | ||
| return False | ||
| lhs = context.annotated_expr["lhs"] | ||
| rhs = context.annotated_expr["rhs"] | ||
| matmul_call = context.annotated_expr["root"] | ||
|
|
||
| lhs_dtype = lhs.struct_info.dtype | ||
| rhs_dtype = rhs.struct_info.dtype | ||
| out_dtype = matmul_call.struct_info.dtype | ||
| if not _is_supported_dtype(lhs_dtype, rhs_dtype, out_dtype): | ||
| return False | ||
|
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| lhs_shape = lhs.struct_info.shape.values | ||
| rhs_shape = rhs.struct_info.shape.values | ||
|
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| if not isinstance(lhs_shape[-1], (tvm.tir.expr.IntImm, int)): | ||
| # Reduction axis must be constant | ||
| return False | ||
|
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| if lhs_dtype == "int8" and rhs_dtype == "int8": | ||
| return False | ||
| elif lhs_dtype == "e4m3_float8" and rhs_dtype == "e4m3_float8": | ||
| return False | ||
|
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| lhs_batches = reduce(operator.mul, lhs_shape[:-2], 1) | ||
| rhs_batches = reduce(operator.mul, rhs_shape[:-2], 1) | ||
|
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| if "bias" in context.annotated_expr: | ||
| if lhs_dtype == "int8" and rhs_dtype == "int8": | ||
| # Non-default epilogue not supported for IGEMM | ||
| return False | ||
| bias = context.annotated_expr["bias"] | ||
| bias_shape = bias.struct_info.shape.values | ||
| bias_batches = reduce(operator.mul, bias_shape[:-1], 1) | ||
| if not isinstance(bias_batches, (tvm.tir.expr.IntImm, int)) or int(bias_batches) > 1: | ||
| # hipblas only supports bias vector | ||
| return False | ||
|
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| # hipblasLt does not seem to support batched GEMM with one of matrices having | ||
| # one batch (with batch_stride 0). So for batched GEMM, the two batch counts | ||
| # must be equal. If lhs is batched but rhs is not, we can use the regular GEMM by | ||
| # flattening all batch axes into the M axis. | ||
| return ( | ||
| isinstance(lhs_batches, tvm.tir.Var) | ||
| or isinstance(rhs_batches, tvm.tir.Var) | ||
| or (int(lhs_batches) == int(rhs_batches)) | ||
| or (lhs_batches >= 1 and rhs_batches == 1) | ||
| ) | ||
|
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|
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| register_patterns( | ||
| [ | ||
| ( | ||
| "hipblas.matmul", | ||
| *make_matmul_pattern( | ||
| with_bias=False, | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_bias", | ||
| *make_matmul_pattern( | ||
| with_bias=True, | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_bias_relu", | ||
| *make_matmul_pattern( | ||
| with_bias=True, | ||
| activation="relax.nn.relu", | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_bias_gelu", | ||
| *make_matmul_pattern( | ||
| with_bias=True, | ||
| activation="relax.nn.gelu", | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_transposed", | ||
| *make_matmul_pattern( | ||
| with_bias=False, | ||
| transposed_rhs=True, | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_transposed_bias", | ||
| *make_matmul_pattern( | ||
| with_bias=True, | ||
| transposed_rhs=True, | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_transposed_bias_relu", | ||
| *make_matmul_pattern( | ||
| with_bias=True, | ||
| activation="relax.nn.relu", | ||
| transposed_rhs=True, | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ( | ||
| "hipblas.matmul_transposed_bias_gelu", | ||
| *make_matmul_pattern( | ||
| with_bias=True, | ||
| activation="relax.nn.gelu", | ||
| transposed_rhs=True, | ||
| ), | ||
| _check_matmul, | ||
| ), | ||
| ] | ||
| ) | ||
|
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| def partition_for_hipblas(mod): | ||
| """ | ||
| Partition the input module into hipblas-supported subgraphs. | ||
| Parameters | ||
| ---------- | ||
| mod: tvm.IRModule | ||
| The IRModule to be partitioned. | ||
| Returns | ||
| ------- | ||
| mod: tvm.IRModule | ||
| The resulting IRModule, containing partitioned subgraphs to be | ||
| offloaded to the hipblas backend. | ||
| """ | ||
|
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||
| patterns = get_patterns_with_prefix("hipblas") | ||
| return transform.FuseOpsByPattern(patterns, bind_constants=False, annotate_codegen=True)(mod) |
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