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"""Pattern table for cuBLAS backend""" import operator from functools import reduce import tvm from tvm import DataType from tvm.arith import Analyzer from tvm.relax import transform from tvm.relax.transform import PatternCheckContext from ..pattern_registry import get_patterns_with_prefix, register_patterns from ..patterns import ( make_matmul_dequantize_pattern, make_matmul_multiply_pattern, make_matmul_pattern, ) from ..utils import has_leaking_intermediate_variables def _is_supported_dtype(lhs_dtype, rhs_dtype, out_dtype): """Check if dtypes in the given workload are supported by cuBLAS BYOC.""" if lhs_dtype == "float8_e4m3fn" and rhs_dtype == "float8_e4m3fn": # The output cannot be 'float8_e5m2' if inputs are 'float8_e4m3fn' return out_dtype != "float8_e5m2" return ( (lhs_dtype == "float16" and rhs_dtype == "float16") or (lhs_dtype == "float32" and rhs_dtype == "float32") or (lhs_dtype == "int8" and rhs_dtype == "int8") or (lhs_dtype == "bfloat16" and rhs_dtype == "bfloat16") ) 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"] if "scale" in context.annotated_expr and "zp" in context.annotated_expr: scale = context.annotated_expr["scale"] zero_point = context.annotated_expr["zp"] # Only scalar values for scale and zero_point are supported. if scale.ty.ndim != 0 or zero_point.ty.ndim != 0: return False # Only zero_point == 0.0 is supported. if zero_point.data.numpy()[()].item() != 0.0: return False lhs_dtype = lhs.ty.dtype rhs_dtype = rhs.ty.dtype out_dtype = matmul_call.ty.dtype if not _is_supported_dtype(lhs_dtype, rhs_dtype, out_dtype): return False lhs_shape = lhs.ty.shape.values rhs_shape = rhs.ty.shape.values if not isinstance(lhs_shape[-1], tvm.tirx.expr.IntImm | int): # Reduction axis must be constant return False if lhs_dtype == "int8" and rhs_dtype == "int8": if lhs_shape[-1] % 4 != 0: # Reduction axis must be multiples of 4 for IGEMM return False if not isinstance(rhs_shape[-1], tvm.tirx.expr.IntImm | int) or rhs_shape[-1] % 4 != 0: # Rows number must be multiples of 4 for IGEMM return False elif lhs_dtype == "float8_e4m3fn" and rhs_dtype == "float8_e4m3fn": matmul_rhs_var = matmul_call.args[1] rhs_transposed = False if matmul_rhs_var in context.matched_bindings: matmul_rhs_call = context.matched_bindings[matmul_rhs_var] assert ( isinstance(matmul_rhs_call, tvm.relax.Call) and matmul_rhs_call.op.name == "relax.permute_dims" ) rhs_transposed = True if not rhs_transposed: # cuBLAS FP8 operations require rhs being transposed return False # cuBLAS FP8 operations require all tensors being aligned to 16 bytes. if ( not isinstance(rhs_shape[-1], tvm.tirx.expr.IntImm | int) or rhs_shape[-1] % (16 // DataType(lhs_dtype).itemsize) != 0 ): return False if ( not isinstance(rhs_shape[-2], tvm.tirx.expr.IntImm | int) or rhs_shape[-2] % (16 // DataType(out_dtype).itemsize) != 0 ): return False lhs_batches = reduce(operator.mul, lhs_shape[:-2], 1) rhs_batches = reduce(operator.mul, rhs_shape[:-2], 1) 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.ty.shape.values bias_batches = reduce(operator.mul, bias_shape[:-1], 1) if not isinstance(bias_batches, tvm.tirx.expr.IntImm | int) or int(bias_batches) > 1: # cuBLAS only supports bias vector return False analyzer = Analyzer() # cuBLASLt 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.tirx.Var) or isinstance(rhs_batches, tvm.tirx.Var) or (analyzer.can_prove_equal(lhs_batches, rhs_batches)) or (analyzer.can_prove(lhs_batches >= 1) and analyzer.can_prove(rhs_batches == 1)) ) register_patterns( [ ( "cublas.matmul", *make_matmul_pattern( with_bias=False, ), _check_matmul, ), ( "cublas.matmul_bias", *make_matmul_pattern( with_bias=True, ), _check_matmul, ), ( "cublas.matmul_bias_relu", *make_matmul_pattern( with_bias=True, activation="relax.nn.relu", ), _check_matmul, ), ( "cublas.matmul_bias_gelu", *make_matmul_pattern( with_bias=True, activation="relax.nn.gelu", ), _check_matmul, ), ( "cublas.matmul_transposed", *make_matmul_pattern( with_bias=False, transposed_rhs=True, ), _check_matmul, ), ( "cublas.matmul_transposed_bias", *make_matmul_pattern( with_bias=True, transposed_rhs=True, ), _check_matmul, ), ( "cublas.matmul_transposed_bias_relu", *make_matmul_pattern( with_bias=True, activation="relax.nn.relu", transposed_rhs=True, ), _check_matmul, ), ( "cublas.matmul_transposed_bias_gelu", *make_matmul_pattern( with_bias=True, activation="relax.nn.gelu", transposed_rhs=True, ), _check_matmul, ), ( "cublas.matmul_transposed_dequantize", *make_matmul_dequantize_pattern(transposed_rhs=True), _check_matmul, ), ( "cublas.matmul_transposed_multiply", *make_matmul_multiply_pattern(transposed_rhs=True), _check_matmul, ), ] ) def partition_for_cublas(mod, bind_constants=False): """ Partition the input module into cuBLAS-supported subgraphs. Parameters ---------- mod: tvm.IRModule The IRModule to be partitioned. bind_constants : bool Whether or not to keep bound constants in the grouped function. Returns ------- mod: tvm.IRModule The resulting IRModule, containing partitioned subgraphs to be offloaded to the cuBLAS backend. """ patterns = get_patterns_with_prefix("cublas") return transform.FuseOpsByPattern( patterns, bind_constants=bind_constants, annotate_codegen=True )(mod)