[ARITH] Allow Analyzer to MarkGlobalNonNegValue #15193
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This PR introduces an utility function MarkGlobalNonNegValue. This function allows analyzer to mark buffer shapes in function arguments as positive globally and opens doors for more symbolic simplification.
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junrushao
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Jul 3, 2023
This PR enhance the arithmetic analysis used in compute-at to further help symbolic bound simplification. Previously, when a variable `n` appears in the shape of an input buffer `T.Buffer((n * 32), "float32")`, we could safely assume that `n` is nonnegative as it is part of the shape. This could help us simplify some bounds during scheduling as well as lowering. For example, for integers `n` and `bx` where `bx` has a symbolic bound `[0, 32 * n)`, if `n` is nonnegative, we could simplify the following expressions to True: ``` 0 <= floordiv(bx, n) < 32 0 <= floormod(bx, n) < n ``` This PR depends on apache#15193 to provide an interface that hints analyzer.
junrushao
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Jul 3, 2023
This PR introduces an utility function MarkGlobalNonNegValue. This function allows analyzer to mark buffer shapes in function arguments as positive globally and opens doors for more symbolic simplification.
junrushao
added a commit
to junrushao/tvm
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this pull request
Jul 3, 2023
This PR enhance the arithmetic analysis used in compute-at to further help symbolic bound simplification. Previously, when a variable `n` appears in the shape of an input buffer `T.Buffer((n * 32), "float32")`, we could safely assume that `n` is nonnegative as it is part of the shape. This could help us simplify some bounds during scheduling as well as lowering. For example, for integers `n` and `bx` where `bx` has a symbolic bound `[0, 32 * n)`, if `n` is nonnegative, we could simplify the following expressions to True: ``` 0 <= floordiv(bx, n) < 32 0 <= floormod(bx, n) < n ``` This PR depends on apache#15193 to provide an interface that hints analyzer.
junrushao
added a commit
to junrushao/tvm
that referenced
this pull request
Jul 3, 2023
This PR enhance the arithmetic analysis used in compute-at to further help symbolic bound simplification. Previously, when a variable `n` appears in the shape of an input buffer `T.Buffer((n * 32), "float32")`, we could safely assume that `n` is nonnegative as it is part of the shape. This could help us simplify some bounds during scheduling as well as lowering. For example, for integers `n` and `bx` where `bx` has a symbolic bound `[0, 32 * n)`, if `n` is nonnegative, we could simplify the following expressions to True: ``` 0 <= floordiv(bx, n) < 32 0 <= floormod(bx, n) < n ``` This PR depends on apache#15193 to provide an interface that hints analyzer.
tqchen
pushed a commit
that referenced
this pull request
Jul 3, 2023
This PR enhance the arithmetic analysis used in compute-at to further help symbolic bound simplification. Previously, when a variable `n` appears in the shape of an input buffer `T.Buffer((n * 32), "float32")`, we could safely assume that `n` is nonnegative as it is part of the shape. This could help us simplify some bounds during scheduling as well as lowering. For example, for integers `n` and `bx` where `bx` has a symbolic bound `[0, 32 * n)`, if `n` is nonnegative, we could simplify the following expressions to True: ``` 0 <= floordiv(bx, n) < 32 0 <= floormod(bx, n) < n ``` This PR depends on #15193 to provide an interface that hints analyzer.
junrushao
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Jul 4, 2023
This PR enhances Decode-GEMV rule with the following changes: - Normalize the GEMV iter domain to S-R-C via transform-block-layout. This would help with further analysis and scheduling, in cases for example, when there was no spatial loop in the original reduction block. - Get rid of the ad hoc iter type analysis, including the logic calling into a TVM packed func `tir.schedule.GetLoopIterType` using `tvm._ffi.get_global_func`. - Split out the logic for two separate cases of scheduling, where the innermost dimension is spatial or reduction. - Introduces `suggest_threads_per_block` to guess the threads to be allocated each threadblock. This helps avoid the previous case where dlight allocates 256 threads for a workload whose degree of parallelism is only 128. - Misc improvements. This rest of the changes are split out to separate PRs that are already merged to main. - [x] Pass the hints to arithmetic analyzer that shape variables should be positive ones (apache#15210) - [x] Eliminate unnecessary block predicate generation - should be provable via affine analysis (apache#15193) - [x] Shrink local memory allocation if only one element `X[threadIdx.x]` is used (apache#15207)
junrushao
added a commit
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Jul 4, 2023
This PR enhances Decode-GEMV rule with the following changes: - Normalize the GEMV iter domain to S-R-C via transform-block-layout. This would help with further analysis and scheduling, in cases for example, when there was no spatial loop in the original reduction block. - Get rid of the ad hoc iter type analysis, including the logic calling into a TVM packed func `tir.schedule.GetLoopIterType` using `tvm._ffi.get_global_func`. - Split out the logic for two separate cases of scheduling, where the innermost dimension is spatial or reduction. - Introduces `suggest_threads_per_block` to guess the threads to be allocated each threadblock. This helps avoid the previous case where dlight allocates 256 threads for a workload whose degree of parallelism is only 128. - Misc improvements. This rest of the changes are split out to separate PRs that are already merged to main. - [x] Pass the hints to arithmetic analyzer that shape variables should be positive ones (apache#15210) - [x] Eliminate unnecessary block predicate generation - should be provable via affine analysis (apache#15193) - [x] Shrink local memory allocation if only one element `X[threadIdx.x]` is used (apache#15207)
junrushao
added a commit
to junrushao/tvm
that referenced
this pull request
Jul 4, 2023
This PR enhances Decode-GEMV rule with the following changes: - Normalize the GEMV iter domain to S-R-C via transform-block-layout. This would help with further analysis and scheduling, in cases for example, when there was no spatial loop in the original reduction block. - Get rid of the ad hoc iter type analysis, including the logic calling into a TVM packed func `tir.schedule.GetLoopIterType` using `tvm._ffi.get_global_func`. - Split out the logic for two separate cases of scheduling, where the innermost dimension is spatial or reduction. - Introduces `suggest_threads_per_block` to guess the threads to be allocated each threadblock. This helps avoid the previous case where dlight allocates 256 threads for a workload whose degree of parallelism is only 128. - Misc improvements. This rest of the changes are split out to separate PRs that are already merged to main. - [x] Pass the hints to arithmetic analyzer that shape variables should be positive ones (apache#15210) - [x] Eliminate unnecessary block predicate generation - should be provable via affine analysis (apache#15193) - [x] Shrink local memory allocation if only one element `X[threadIdx.x]` is used (apache#15207)
junrushao
added a commit
to junrushao/tvm
that referenced
this pull request
Jul 4, 2023
This PR enhances Decode-GEMV rule with the following changes: - Normalize the GEMV iter domain to S-R-C via transform-block-layout. This would help with further analysis and scheduling, in cases for example, when there was no spatial loop in the original reduction block. - Get rid of the ad hoc iter type analysis, including the logic calling into a TVM packed func `tir.schedule.GetLoopIterType` using `tvm._ffi.get_global_func`. - Split out the logic for two separate cases of scheduling, where the innermost dimension is spatial or reduction. - Introduces `suggest_threads_per_block` to guess the threads to be allocated each threadblock. This helps avoid the previous case where dlight allocates 256 threads for a workload whose degree of parallelism is only 128. - Misc improvements. This rest of the changes are split out to separate PRs that are already merged to main. - [x] Pass the hints to arithmetic analyzer that shape variables should be positive ones (apache#15210) - [x] Eliminate unnecessary block predicate generation - should be provable via affine analysis (apache#15193) - [x] Shrink local memory allocation if only one element `X[threadIdx.x]` is used (apache#15207)
junrushao
added a commit
to junrushao/tvm
that referenced
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Jul 5, 2023
This PR enhances Decode-GEMV rule with the following changes: - Normalize the GEMV iter domain to S-R-C via transform-block-layout. This would help with further analysis and scheduling, in cases for example, when there was no spatial loop in the original reduction block. - Get rid of the ad hoc iter type analysis, including the logic calling into a TVM packed func `tir.schedule.GetLoopIterType` using `tvm._ffi.get_global_func`. - Split out the logic for two separate cases of scheduling, where the innermost dimension is spatial or reduction. - Introduces `suggest_threads_per_block` to guess the threads to be allocated each threadblock. This helps avoid the previous case where dlight allocates 256 threads for a workload whose degree of parallelism is only 128. - Misc improvements. This rest of the changes are split out to separate PRs that are already merged to main. - [x] Pass the hints to arithmetic analyzer that shape variables should be positive ones (apache#15210) - [x] Eliminate unnecessary block predicate generation - should be provable via affine analysis (apache#15193) - [x] Shrink local memory allocation if only one element `X[threadIdx.x]` is used (apache#15207)
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This PR introduces an utility function MarkGlobalPositiveValue. This function allows analyzer to mark buffer shapes in function arguments as positive globally and opens doors for more symbolic simplification.