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expand_as through the existing shared-qparams expand pathexpand_as through the existing shared-qparams expand path (#24153)
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pytorch#24153) Summary: `SharedQspecQuantizer` already gives `expand` and `expand_copy` shared qparams when a neighbor is quantized, and the lowering then runs `expand_copy` on int8. `expand_as` is not in that list, and it only decomposes to `expand` at `to_edge`, after quantization, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. `ExpandAsToExpandPass` runs in `transform_for_annotation` and rewrites `expand_as(x, other)` into `expand(x, other.shape)`, emitting `sym_size` nodes for symbolic dims. `expand_as` then gets the same neighbor-driven treatment as `expand`: int8 when its neighbors are quantized, float otherwise. The shape-only `other` operand is no longer an input of the op, so it is never pulled into the shared-qparams cluster. No new pattern check or support-dict entry is added for `expand`, so an expand between float-only ops is not forced to int8. Differential Revision: D127380012
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pytorch#24153) Summary: `SharedQspecQuantizer` already gives `expand` and `expand_copy` shared qparams when a neighbor is quantized, and the lowering then runs `expand_copy` on int8. `expand_as` is not in that list, and it only decomposes to `expand` at `to_edge`, after quantization, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. `ExpandAsToExpandPass` runs in `transform_for_annotation` and rewrites `expand_as(x, other)` into `expand(x, other.shape)`, emitting `sym_size` nodes for symbolic dims. `expand_as` then gets the same neighbor-driven treatment as `expand`: int8 when its neighbors are quantized, float otherwise. The shape-only `other` operand is no longer an input of the op, so it is never pulled into the shared-qparams cluster. No new pattern check or support-dict entry is added for `expand`, so an expand between float-only ops is not forced to int8. Differential Revision: D127380012
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pytorch#24153) Summary: `SharedQspecQuantizer` already gives `expand` and `expand_copy` shared qparams when a neighbor is quantized, and the lowering then runs `expand_copy` on int8. `expand_as` is not in that list, and it only decomposes to `expand` at `to_edge`, after quantization, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. `ExpandAsToExpandPass` runs in `transform_for_annotation` and rewrites `expand_as(x, other)` into `expand(x, other.shape)`, emitting `sym_size` nodes for symbolic dims. `expand_as` then gets the same neighbor-driven treatment as `expand`: int8 when its neighbors are quantized, float otherwise. The shape-only `other` operand is no longer an input of the op, so it is never pulled into the shared-qparams cluster. No new pattern check or support-dict entry is added for `expand`, so an expand between float-only ops is not forced to int8. Differential Revision: D127380012
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pytorch#24153) Summary: `SharedQspecQuantizer` already gives `expand` and `expand_copy` shared qparams when a neighbor is quantized, and the lowering then runs `expand_copy` on int8. `expand_as` is not in that list, and it only decomposes to `expand` at `to_edge`, after quantization, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. `ExpandAsToExpandPass` runs in `transform_for_annotation` and rewrites `expand_as(x, other)` into `expand(x, other.shape)`, emitting `sym_size` nodes for symbolic dims. `expand_as` then gets the same neighbor-driven treatment as `expand`: int8 when its neighbors are quantized, float otherwise. The shape-only `other` operand is no longer an input of the op, so it is never pulled into the shared-qparams cluster. No new pattern check or support-dict entry is added for `expand`, so an expand between float-only ops is not forced to int8. Differential Revision: D127380012
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pytorch#24153) Summary: `SharedQspecQuantizer` already gives `expand` and `expand_copy` shared qparams when a neighbor is quantized, and the lowering then runs `expand_copy` on int8. `expand_as` is not in that list, and it only decomposes to `expand` at `to_edge`, after quantization, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. `ExpandAsToExpandPass` runs in `transform_for_annotation` and rewrites `expand_as(x, other)` into `expand(x, other.shape)`, emitting `sym_size` nodes for symbolic dims. `expand_as` then gets the same neighbor-driven treatment as `expand`: int8 when its neighbors are quantized, float otherwise. The shape-only `other` operand is no longer an input of the op, so it is never pulled into the shared-qparams cluster. No new pattern check or support-dict entry is added for `expand`, so an expand between float-only ops is not forced to int8. Differential Revision: D127380012
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expand_as through the existing shared-qparams expand path (#24153)
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…pytorch#24153) Summary: `SharedQspecQuantizer` gives each shared-qparams cluster one set of qparams for every float input and output of its ops. `view_as(x, other)` is in that list, and the cluster was built from all of its inputs, so `other` was pulled in even though it contributes only its shape. `x` and `other` were then quantized with one scale, set by whichever has the wider range: the data loses precision, or the shape operand is clipped. For example, in `(x * y).view_as(other) + other` with `other = z + z`, `x * y` spans about ±2.5 and `other` about ±51. `x * y` was quantized at `other`'s scale of 0.349, about 18x coarser than the 0.0195 it gets when the same reshape is written `reshape(4, 8)`, and the output error against float rose from 0.258 to 0.307. `expand_as` has the same shape-only second operand but was not in the shared-qparams list at all, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. Shape-as ops (`view_as`, `expand_as`) now share qparams only through their data operand, `args[0]`. The cluster walk does not cross the edge to the shape operand in either direction, and that operand gets no shared input qspec, so it keeps the qparams of its own producer. `expand_as` joins the shared-qparams ops, so like `expand` it is quantized to int8 when its neighbours are, and lowers on Cortex-M to an int8 `expand_copy`. Authored with Claude Code. Differential Revision: D127380012
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…pytorch#24153) Summary: `SharedQspecQuantizer` gives each shared-qparams cluster one set of qparams for every float input and output of its ops. `view_as(x, other)` is in that list, and the cluster was built from all of its inputs, so `other` was pulled in even though it contributes only its shape. `x` and `other` were then quantized with one scale, set by whichever has the wider range: the data loses precision, or the shape operand is clipped. For example, in `(x * y).view_as(other) + other` with `other = z + z`, `x * y` spans about ±2.5 and `other` about ±51. `x * y` was quantized at `other`'s scale of 0.349, about 18x coarser than the 0.0195 it gets when the same reshape is written `reshape(4, 8)`, and the output error against float rose from 0.258 to 0.307. `expand_as` has the same shape-only second operand but was not in the shared-qparams list at all, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. Shape-as ops (`view_as`, `expand_as`) now share qparams only through their data operand, `args[0]`. The cluster walk does not cross the edge to the shape operand in either direction, and that operand gets no shared input qspec, so it keeps the qparams of its own producer. `expand_as` joins the shared-qparams ops, so like `expand` it is quantized to int8 when its neighbours are, and lowers on Cortex-M to an int8 `expand_copy`. Authored with Claude Code. Differential Revision: D127380012
Summary: Q/DQ folding (`FoldAndAnnotateQParamsPass`) always folds the quantize/dequantize pair around a constant-producing op (`full`, `full_like`, `arange`, `eye`, `linspace`, `scalar_tensor`) into the op: it removes the Q node and records the output qparams, but the op itself keeps producing float. The Arm backend follows folding with `ComputeConstantOpsAOTPass`, which evaluates such ops in float and quantizes the result. Cortex-M did not, so in `x + torch.full(shape, 0.5)` the int8 `quantized_add` kernel read a float32 constant, and the output was silently wrong. Add `ComputeQuantizedConstantOpsPass` and run it after folding on both Cortex-M pass lists. - a. It only touches nodes with output qparams, so unquantized constant ops are unchanged. A quantized `full` stays a runtime fill, with its fill value quantized and its dtype set to the qparams' dtype, so a large fill does not turn into a large constant; - b.`full_like` is first converted to `full` with the Arm backend's `ConvertFullLikeToFullPass`. This also fixes a test that was already failing. `surrounded_quantized_op_constant` in `misc/test_quantization.py` was marked as an expected failure. Its `torch.ones(2, 2)` constant is lowered to a `full` op, so the int8 add read float data, and every output element was off by about 0.49. With this change the case matches the quantized reference exactly, so its `xfail` is removed. The other constants in that file are stored tensors, which folding already quantized correctly. Differential Revision: D128002647
…pytorch#24153) Summary: `SharedQspecQuantizer` gives each shared-qparams cluster one set of qparams for every float input and output of its ops. `view_as(x, other)` is in that list, and the cluster was built from all of its inputs, so `other` was pulled in even though it contributes only its shape. `x` and `other` were then quantized with one scale, set by whichever has the wider range: the data loses precision, or the shape operand is clipped. For example, in `(x * y).view_as(other) + other` with `other = z + z`, `x * y` spans about ±2.5 and `other` about ±51. `x * y` was quantized at `other`'s scale of 0.349, about 18x coarser than the 0.0195 it gets when the same reshape is written `reshape(4, 8)`, and the output error against float rose from 0.258 to 0.307. `expand_as` has the same shape-only second operand but was not in the shared-qparams list at all, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. Shape-as ops (`view_as`, `expand_as`) now share qparams only through their data operand, `args[0]`. The cluster walk does not cross the edge to the shape operand in either direction, and that operand gets no shared input qspec, so it keeps the qparams of its own producer. `expand_as` joins the shared-qparams ops, so like `expand` it is quantized to int8 when its neighbours are, and lowers on Cortex-M to an int8 `expand_copy`. Authored with Claude Code. Differential Revision: D127380012
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…pytorch#24153) Summary: `SharedQspecQuantizer` gives each shared-qparams cluster one set of qparams for every float input and output of its ops. `view_as(x, other)` is in that list, and the cluster was built from all of its inputs, so `other` was pulled in even though it contributes only its shape. `x` and `other` were then quantized with one scale, set by whichever has the wider range: the data loses precision, or the shape operand is clipped. For example, in `(x * y).view_as(other) + other` with `other = z + z`, `x * y` spans about ±2.5 and `other` about ±51. `x * y` was quantized at `other`'s scale of 0.349, about 18x coarser than the 0.0195 it gets when the same reshape is written `reshape(4, 8)`, and the output error against float rose from 0.258 to 0.307. `expand_as` has the same shape-only second operand but was not in the shared-qparams list at all, so every `expand_as` broadcast stayed in float behind a dequantize and a requantize. Shape-as ops (`view_as`, `expand_as`) now share qparams only through their data operand, `args[0]`. The cluster walk does not cross the edge to the shape operand in either direction, and that operand gets no shared input qspec, so it keeps the qparams of its own producer. `expand_as` joins the shared-qparams ops, so like `expand` it is quantized to int8 when its neighbours are, and lowers on Cortex-M to an int8 `expand_copy`. Authored with Claude Code. Differential Revision: D127380012
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Summary:
SharedQspecQuantizergives each shared-qparams cluster one set of qparams for every float input and output of its ops.view_as(x, other)is in that list, and the cluster was built from all of its inputs, sootherwas pulled in even though it contributes only its shape.xandotherwere then quantized with one scale, set by whichever has the wider range: the data loses precision, or the shape operand is clipped.For example, in
(x * y).view_as(other) + otherwithother = z + z,x * yspans about ±2.5 andotherabout ±51.x * ywas quantized atother's scale of 0.349, about 18x coarser than the 0.0195 it gets when the same reshape is writtenreshape(4, 8), and the output error against float rose from 0.258 to 0.307.expand_ashas the same shape-only second operand but was not in the shared-qparams list at all, so everyexpand_asbroadcast stayed in float behind a dequantize and a requantize.Shape-as ops (
view_as,expand_as) now share qparams only through their data operand,args[0]. The cluster walk does not cross the edge to the shape operand in either direction, and that operand gets no shared input qspec, so it keeps the qparams of its own producer.expand_asjoins the shared-qparams ops, so likeexpandit is quantized to int8 when its neighbours are, and lowers on Cortex-M to an int8expand_copy.Authored with Claude Code.
Differential Revision: D127380012